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Storybook V2 Comic Book Builder: Part 2, The Document Generator

An Elegant Three Button HTML and JavaScript Audio Animation Player

Using CSS to Create a GIF Animation

The Storybook Landing Page: A CSS/JavaScript Animated Splash Screen Landing Page

As the founder of ScaleVoice, I'm leveraging my background in AI and automotive e-commerce to revolutionize sales operations. Our company provides autonomous AI voice sales agents that act as "superhero sidekicks" for automotive sales teams, handling outbound calls, SMS, and chat conversations with human-like proficiency. With a track record of facilitating more than $7 million in monthly car purchases for our clients and processing over one million calls per month, ScaleVoice is at the forefront of AI-powered sales solutions, delivering tangible results and redefining customer engagement in the automotive industry. 1. What is your company in 2–5 words? Ondrej Kuchta: Autonomous AI voice sales agent 2. Why is now the time for your company to exist? OK: VC firm Andreessen Horowitz recently stated, "We believe AI will fundamentally reimagine core systems and sales workflows, leaving no incumbent safe." This paradigm shift presents opportunities for companies like ScaleVoice that specialize in complex, long-term customer journeys. ScaleVoice combines cutting-edge AI expertise with deep automotive sales knowledge. Unlike traditional chatbots that follow strict scripts, our AI agents make decisions based on the conversation context. We focus on delivering tangible results rather than just selling software. Our approach manages end-to-end workflows, from initial customer contact through appointment scheduling and follow-ups. This comprehensive solution sets us apart in the market, where many competitors offer more limited functionality. 3. What do you love about your team, and why are you the ones to solve this problem? OK: I believe we are at the forefront of combining cutting-edge AI expertise with deep industry knowledge. We're not just tech experts - we're domain specialists who understand the unique challenges facing businesses in the automotive space. Our team's blend of skills and experience is what makes us uniquely qualified. We've got seasoned AI researchers and engineers working alongside former automotive executives and sales professionals. This cross-pollination allows us to develop solutions that are technologically sophisticated and grounded in real-world practicality. Equally important, we're passionate problem-solvers driven by results. This outcome-focused mindset permeates everything we do. 4. If you weren’t building your startup, what would you be doing? OK: I'd likely explore other AI applications in business or work on another transformative tech startup. My background spans roles from kernel development to founding my own startups in fitness, retail and home security.Prior to ScaleVoice, I served as the CTO at Driverama, a Germany-based e-commerce platform using data to facilitate used car transactions in Western Europe. Under my leadership, we experienced remarkable growth, expanding from four employees to 250 and increasing revenue from zero to $40 million in less than two years. My success in helping auto dealerships acquire vehicles at Driverama revealed even more opportunities to leverage cutting-edge AI technologies that previously weren't sufficiently advanced or didn't exist. These experiences, combined with the emergence of powerful new AI tools, made the timing perfect for launching ScaleVoice. So while I'm always open to new opportunities, I'm currently laser-focused on ScaleVoice's mission to redefine the future of sales through AI-powered operations. The potential is limitless, and I can't imagine doing anything else. 5. At the moment, how do you measure success? What are your metrics? OK: We have a unique success-based pricing model, which means we only make money when our clients make a sale. Our key metrics include sales volume for our customers, agent success rate, lead response time, and overall call volume which currently exceeds one million per month. At the end of the day, we're not selling software - we're delivering work. 6. In a few sentences, what do you offer to whom? OK: ScaleVoice acts as a "superhero sidekick" for sales teams in automotive and related sectors. Our autonomous AI agents handle outbound calls, SMS, and chat conversations as well as humans. They contact potential car sellers within seconds of their expressed interest, which helps convert more leads into qualified appointments. Most customers don't even realize they're interacting with an AI agent, which means more efficient, consistent conversations and happier customers overall. 7. What’s most exciting about your traction to date? The most exciting aspect of our traction is the consistent, measurable performance of our AI agents in real-world applications. Our work with Aures, the largest Central European dealer of pre-owned cars, has been a particularly significant milestone for us. We're currently facilitating more than $7 million in monthly car purchases for Aures. This impressive figure demonstrates the scalability and effectiveness of our product, even in uncertain market conditions. These kinds of results truly showcase the impact of our technology. Our AI agents aren't just talking the talk - they're delivering tangible business results for our clients. It's exciting to see our solution making such a substantial difference in the automotive sales landscape. 8. Where do you think your growth will be next year? Next year, we expect significant growth as we expand into the US market this fall, targeting states with high concentrations of car dealerships like California, New York, and Texas. We're confident that our proven track record in Europe will translate well to the American market, where businesses are equally hungry for innovative solutions to streamline their customer operations.We also plan to expand into related verticals like car insurance, financing and even the energy sector. These are ambitious moves, but we’ll never shy away from going after challenging verticals as the demand for our technology continues to grow. 9. Tell us about your first paying customer and revenue expectations over the next year. While I can't disclose specific client details, our success with Aures is indicative of our revenue potential. 10. What’s your biggest threat? One of our biggest threats is the rapid pace of AI development and potential competition. We’re staying ahead by continuously enhancing our AI-driven solution to provide even greater value to dealerships and sales teams. We're also developing deeper integrations with management systems and CRMs, enabling more tailored interactions and ability to handle even more complex workflows. For example, our system will soon be able to recognize when a customer's vehicle needs a tire change and facilitate that service on the spot. Beyond single calls, we're developing our AI's ability to manage and convert customers across a series of interactions, creating a cohesive and long-term engagement strategy. Ultimately, our deep industry understanding and laser-focus on tangible outcomes are our biggest advantages. While competitors may try to catch up, we're committed to staying steps ahead through continuous innovation and an unwavering commitment to results. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. Would you like to take a stab at answering some of these questions? The link for the template is HERE. As the founder of ScaleVoice, I'm leveraging my background in AI and automotive e-commerce to revolutionize sales operations. Our company provides autonomous AI voice sales agents that act as "superhero sidekicks" for automotive sales teams, handling outbound calls, SMS, and chat conversations with human-like proficiency. With a track record of facilitating more than $7 million in monthly car purchases for our clients and processing over one million calls per month, ScaleVoice is at the forefront of AI-powered sales solutions, delivering tangible results and redefining customer engagement in the automotive industry. 1. What is your company in 2–5 words? Ondrej Kuchta: Autonomous AI voice sales agent 2. Why is now the time for your company to exist? OK: VC firm Andreessen Horowitz recently stated , "We believe AI will fundamentally reimagine core systems and sales workflows, leaving no incumbent safe." This paradigm shift presents opportunities for companies like ScaleVoice that specialize in complex, long-term customer journeys. Andreessen Horowitz recently stated Andreessen Horowitz recently stated ScaleVoice ScaleVoice ScaleVoice combines cutting-edge AI expertise with deep automotive sales knowledge. Unlike traditional chatbots that follow strict scripts, our AI agents make decisions based on the conversation context. We focus on delivering tangible results rather than just selling software. Our approach manages end-to-end workflows, from initial customer contact through appointment scheduling and follow-ups. This comprehensive solution sets us apart in the market, where many competitors offer more limited functionality. 3. What do you love about your team, and why are you the ones to solve this problem? OK: I believe we are at the forefront of combining cutting-edge AI expertise with deep industry knowledge. We're not just tech experts - we're domain specialists who understand the unique challenges facing businesses in the automotive space. Our team's blend of skills and experience is what makes us uniquely qualified. We've got seasoned AI researchers and engineers working alongside former automotive executives and sales professionals. This cross-pollination allows us to develop solutions that are technologically sophisticated and grounded in real-world practicality. Equally important, we're passionate problem-solvers driven by results. This outcome-focused mindset permeates everything we do. 4. If you weren’t building your startup, what would you be doing? OK: I'd likely explore other AI applications in business or work on another transformative tech startup. My background spans roles from kernel development to founding my own startups in fitness, retail and home security. Prior to ScaleVoice, I served as the CTO at Driverama, a Germany-based e-commerce platform using data to facilitate used car transactions in Western Europe. Under my leadership, we experienced remarkable growth, expanding from four employees to 250 and increasing revenue from zero to $40 million in less than two years. My success in helping auto dealerships acquire vehicles at Driverama revealed even more opportunities to leverage cutting-edge AI technologies that previously weren't sufficiently advanced or didn't exist. These experiences, combined with the emergence of powerful new AI tools, made the timing perfect for launching ScaleVoice. So while I'm always open to new opportunities, I'm currently laser-focused on ScaleVoice's mission to redefine the future of sales through AI-powered operations. The potential is limitless, and I can't imagine doing anything else. 5. At the moment, how do you measure success? What are your metrics? OK: We have a unique success-based pricing model, which means we only make money when our clients make a sale. Our key metrics include sales volume for our customers, agent success rate, lead response time, and overall call volume which currently exceeds one million per month. At the end of the day, we're not selling software - we're delivering work. 6. In a few sentences, what do you offer to whom? OK: ScaleVoice acts as a "superhero sidekick" for sales teams in automotive and related sectors. Our autonomous AI agents handle outbound calls, SMS, and chat conversations as well as humans. They contact potential car sellers within seconds of their expressed interest, which helps convert more leads into qualified appointments. Most customers don't even realize they're interacting with an AI agent, which means more efficient, consistent conversations and happier customers overall. 7. What’s most exciting about your traction to date? The most exciting aspect of our traction is the consistent, measurable performance of our AI agents in real-world applications. Our work with Aures , the largest Central European dealer of pre-owned cars, has been a particularly significant milestone for us. Our work with Aures Our work with Aures We're currently facilitating more than $7 million in monthly car purchases for Aures. This impressive figure demonstrates the scalability and effectiveness of our product, even in uncertain market conditions. These kinds of results truly showcase the impact of our technology. Our AI agents aren't just talking the talk - they're delivering tangible business results for our clients. It's exciting to see our solution making such a substantial difference in the automotive sales landscape. 8. Where do you think your growth will be next year? Next year, we expect significant growth as we expand into the US market this fall, targeting states with high concentrations of car dealerships like California, New York, and Texas. We're confident that our proven track record in Europe will translate well to the American market, where businesses are equally hungry for innovative solutions to streamline their customer operations.We also plan to expand into related verticals like car insurance, financing and even the energy sector. These are ambitious moves, but we’ll never shy away from going after challenging verticals as the demand for our technology continues to grow. 9. Tell us about your first paying customer and revenue expectations over the next year. While I can't disclose specific client details, our success with Aures is indicative of our revenue potential. 10. What’s your biggest threat? One of our biggest threats is the rapid pace of AI development and potential competition. We’re staying ahead by continuously enhancing our AI-driven solution to provide even greater value to dealerships and sales teams. We're also developing deeper integrations with management systems and CRMs, enabling more tailored interactions and ability to handle even more complex workflows. For example, our system will soon be able to recognize when a customer's vehicle needs a tire change and facilitate that service on the spot. Beyond single calls, we're developing our AI's ability to manage and convert customers across a series of interactions, creating a cohesive and long-term engagement strategy. Ultimately, our deep industry understanding and laser-focus on tangible outcomes are our biggest advantages. While competitors may try to catch up, we're committed to staying steps ahead through continuous innovation and an unwavering commitment to results. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke ’s ten questions for startup founders . HackerNoon Founder & CEO David Smooke ten questions for startup founders Would you like to take a stab at answering some of these questions? The link for the template is HERE. Would you like to take a stab at answering some of these questions? The link for the template is HERE. The link for the template is HERE.

10 Questions for Every Startup Founder with ScaleVoice Co-founder & CEO, Ondrej Kuchta

A New Approach to Funding SaaS Companies By Lew Moorman, GP/co-founder Scaleworks During the deployment of our first $60M venture equity fund, we came across so many good software companies that were not a great fit for traditional venture capital. Some were not growing quite fast enough to be viewed as “venture scale”, others wanted to take a more controlled invest as you grow approach. As we studied these situations case-by-case, one thing became very clear: injection of capital could absolutely accelerate their momentum. However, right now there are not many options for companies at that stage — some continue to bootstrap and miss their opportunity, and others raise VC even though they may not really be suitable for that path. some do Unfortunately, traditional banks still require consistent cash flow or tangible assets to collateralize any loan, something that growing SaaS companies typically don’t have. They do, however, have consistent recurring revenue streams, with high gross margins. We believe that is a bankable asset, banks don’t. So we thought…hey, we can provide yet another path for founders and teams with great products. We call it our “venture finance” fund, and we’re ready to deploy $10M immediately. Karl Pichler, GP, Venture Finance The fund will make loans to growing SaaS companies in value of up to 6 months of their recurring revenue. To manage the fund, we’ve brought in Rackspace’s former CFO of 6 years, Karl Pichler as GP, Venture Finance. His experience with financing and advising companies on lowering its cost of capital is second to none. He has also seen the same pushback from banks that we have. Now we get to work on solving these problems together! We’re looking forward to hearing from companies who want to chat about their place in the SaaS world and how we can help scale them from great products to great businesses. Whether we take a majority equity position or help with debt financing, we hope to continue to build a community of SaaS companies building smart and sustainable businesses. Lew and team Scaleworks

Introducing the Scaleworks Venture Finance Fund

Flighty App Can Now Predict Flight Delays: Here’s How You Can Also Do It Using Machine Learning

ফ্লাইটি অ্যাপ এখন ফ্লাইট বিলম্বের পূর্বাভাস দিতে পারে: এখানে আপনি কীভাবে মেশিন লার্নিং ব্যবহার করে এটি করতে পারেন

Die Flighty-App kann jetzt Flugverspätungen vorhersagen: So können Sie dies auch mithilfe von maschinellem Lernen tun

La aplicación Flighty ahora puede predecir retrasos en los vuelos: así es como puedes hacerlo tú también mediante aprendizaje automático

L’application Flighty peut désormais prédire les retards de vol : voici comment vous pouvez également le faire grâce au machine learning

फ्लाइटी ऐप अब उड़ान में देरी का अनुमान लगा सकता है: यहां बताया गया है कि आप मशीन लर्निंग का उपयोग करके ऐसा कैसे कर सकते हैं

Flighty アプリがフライトの遅延を予測できるようになりました。機械学習を使用してこれを行う方法は次のとおりです。

Flighty 앱이 이제 항공편 지연을 예측할 수 있습니다. 머신 러닝을 사용하여 예측하는 방법도 있습니다.

O aplicativo Flighty agora pode prever atrasos de voos: veja como você também pode fazer isso usando aprendizado de máquina

Приложение Flighty теперь может предсказывать задержки рейсов: вот как это можно сделать с помощью машинного обучения

Flighty Uygulaması Artık Uçuş Gecikmelerini Tahmin Edebiliyor: Makine Öğrenimini Kullanarak Bunu Nasıl Yapabileceğinizi Öğrenin

Ứng dụng Flighty hiện có thể dự đoán sự chậm trễ của chuyến bay: Đây là cách bạn cũng có thể thực hiện bằng cách sử dụng máy học

Flighty 应用程序现在可以预测航班延误：您也可以使用机器学习来实现这一点

Score Big With Football Analytics: Design Your Country's Perfect National Team with Machine Learning

A new theory from a YouTuber suggests Satoshi Nakamoto, the founder of Bitcoin may comprise of three people who merged their first and last names. This is contrary to previous theories that suggest Satoshi is just one person. Certain individuals suspected to be Satoshi may not have comprised of the ‘persons’ that founded Bitcoin. Of course, you've heard of Bitcoin. You've also heard that its creator is the mysterious Satoshi Nakamoto. This theory emerged on October 31st, 2008, after the Bitcoin white paper was released via a cypherpunk mailing list under the name Satoshi Nakamoto, leaving the world to speculate whether it was an individual or a group. New evidence has brought us closer to identifying who Satoshi is. Many believe Satoshi Nakamoto is just one person; well, we are about to find out. Satoshi Nakamoto Comprises of 3 persons - New Evidence Reveals Recent evidence presented by a Youtuber - Fooch, suggests that Hal Finney, the first person to receive Bitcoin, and Satoshi Nakamoto, the pseudonymous creator of Bitcoin, may have attended the same conference, Crypto '98, in Santa Barbara, California in 1998. By checking the event's program info, Fooch discovers a Satoshi in attendance. Searching for the term "Naka" leads him to a page detailing a presentation by Satoshi Hada and Toshiaki Tanaka on The Existence of 3-round Zero-knowledge Protocols at Crypto '98. That's not all. Fooch then searched on Google for Satoshi Hada Nakamoto, and boom, this post on X popped up, showing that the creators of Bitcoin allegedly comprise three researchers who may have also worked with IBM. The archived link leads to an IBM research paper. “Satoshi is allegedly 3 researchers from IBM”. Fooch said. Also, Fooch’s check on Toshiaki Tanaka and Naohiko Uramoto shows that they, at one time, worked at IBM. Right under the post is a comment tagging what seems to be Satoshi Hada’s account. Here's what it looks like 👇🏽 Maybe a coincidence 🧐 Under Satoshi Hada's most recent post on X, which was in 2017 by the way, someone commented by typing 'Satoshi Nakamoto' backward. What does this person know? Also, Satoshi Hada's previous repost on X in 2012 was about the IBM CEO predicting the future of business. Fooch's intelligent investigation was able to connect the dots based on an interview with Fran, Hal Finney’s wife. She recalled her husband communicating with someone named Satoshi, whom he believed to be based in Japan. Evidence from a YouTube video by The Bitcoin Layer corroborates that Satoshi’s working hours align with the PST time zone which is used in Japan. If “They” Are Satoshi, Why Are They Hiding? Fooch is of the opinion that the Japanese are humble people unlike Americans or Australians who may want to prove anything. Well, what do you think about Fooch’s theory? It appears to be the closest sketch to drawing Satoshi Nakamoto though. Another interesting thing that can be noticed is Satoshi's use of “we” in the bitcoin whitepaper - “We propose a…” Other Alleged Satoshis In an adventure to discover the genius behind Bitcoin, various theories have emerged. Some individuals have purposely claimed to be Satoshi despite evidence proving otherwise. Others have been linked to Satoshi because of their early association with Bitcoin, and one unlucky fellow was accused due to a name coincidence. Let's find out who they are and why they may not be 'Satoshi’. Craig Wright An Australian scientist who has been ruled as a fraudster by a judge in the United Kingdom has long claimed to be the founder of Bitcoin. However, his claims lack tangible evidence beyond document forgery, and he has offered many excuses for his inability to prove it, including 'stomping on the hard drive’ that contains files to prove that he created Bitcoin. Dorian Nakamoto When you google Satoshi Nakamoto, the image you often see is that of Dorian Nakamoto, who has denied being the founder of Bitcoin despite the name coincidence. Dorian is a retired physicist. In a 2014 exposé with Newsweek, where he was accused of being the founder of Bitcoin, he claimed he was wrongly quoted when asked about Bitcoin, thinking the question related to his career, and said: “I am no longer involved in that and I cannot discuss it.” When the exposé went viral, Dorian stated that he felt victimized and decided to tell his own side of the story. “I’m saying I’m no longer in engineering. That’s it. And even if I was, when we get hired, you have to sign this document, contract saying you will not reveal anything we divulge during and after employment. So that’s what I implied.
It sounded like I was involved before with bitcoin and looked like I’m not involved now. That’s not what I meant. I want to clarify that.” Dorian's side of the story attracted sympathy from the Bitcoin community, resulting in donations of over 102 bitcoins which is worth almost a hundred million dollars today. Gavin Andersen It's understandable why some might have suspected Gavin Andresen of being Satoshi Nakamoto. He joined Bitcoin as a core developer in its early stages, creating important features that remain crucial to the Bitcoin ecosystem today. In the documentary "The Bitcoin Story," Gavin repeatedly referred to Satoshi as a "he" and emphasized Satoshi's genius: “He's obviously had some academic training because he wrote this academic whitepaper that is in the style of a peer reviewed academic journal article. He's obviously a genius. I’d love to meet him someday.” “As far as I know, no one has actually met Satoshi. No one knows who he, or she, or they are. I've never talked with Satoshi although I have communicated with him electronically via email or on online forums.
Satoshi started working on Bitcoin he says in 2007, and it actually took him a couple of years to convince himself that he could actually create a system that would work.” Gavin believes he was the last person to speak with Satoshi: “I don't know who might have had the last interaction with Satoshi, it might have possibly been me. My very last email to Satoshi, I actually told him I had been invited to give a talk at the CIA. He had already told me he was going to step back. I don't know if me telling him I was going to visit the CIA was the reason I never heard from him again but I certainly think it didn't help.” Hal Finney If you're a Bitcoin enthusiast, you've likely heard of Hal Finney. He was the first person to receive Bitcoin in a transaction, receiving 10 bitcoins from Satoshi Nakamoto on January 12, 2009. This marked him as an early supporter and contributor to the project. As a cryptographer, Finney recognized the potential of Bitcoin, predicted that it would hit $1 million, and actively contributed to its development. He was a passionate advocate for privacy and saw Bitcoin as a solution to financial transaction privacy concerns. Sadly, Hal passed away on August 28, 2014, after battling with complications from Amyotrophic Lateral Sclerosis (ALS) for five years. Recent research by Jameson Lopp, a Bitcoin researcher, has provided evidence that Hal Finney was not Satoshi Nakamoto. This research analyzed the timing of a specific Bitcoin transaction and Hal Finney's participation in a race, concluding that they occurred simultaneously, making it impossible for him to have been Satoshi at that time. Adam Back British cryptographer and CEO of Blockstream has also been suspected to be Satoshi because of his early involvement with Bitcoin. In 1997, Adam developed Hashcash, a proof-of-work system that is now used in Bitcoin mining. This invention is referenced in Satoshi Nakamoto's Bitcoin white paper. Through public emails revealed between Satoshi and Adam, it appears both individuals have distinct communication styles and technical expertise. Len Sasseman Len Sassaman, a renowned cryptographer and academic researcher, made significant contributions to the field of cryptography and privacy. While his work indirectly influenced the development of Bitcoin, there is no concrete evidence to support the claim that he was Satoshi Nakamoto, the pseudonymous creator of Bitcoin. After Sassaman's untimely death in 2011, the Bitcoin community honored him in a unique and lasting way. A tribute was embedded in block 138725 of the Bitcoin blockchain, forever commemorating his legacy. A message in the block reads: "Len was our friend.A brilliant mind, a kind soul, and a devious schemer.” A new theory from a YouTuber suggests Satoshi Nakamoto, the founder of Bitcoin may comprise of three people who merged their first and last names. This is contrary to previous theories that suggest Satoshi is just one person. A new theory from a YouTuber suggests Satoshi Nakamoto, the founder of Bitcoin may comprise of three people who merged their first and last names. This is contrary to previous theories that suggest Satoshi is just one person. A new theory from a YouTuber suggests Satoshi Nakamoto, the founder of Bitcoin may comprise of three people who merged their first and last names. This is contrary to previous theories that suggest Satoshi is just one person. Certain individuals suspected to be Satoshi may not have comprised of the ‘persons’ that founded Bitcoin. Certain individuals suspected to be Satoshi may not have comprised of the ‘persons’ that founded Bitcoin . Certain individuals suspected to be Satoshi may not have comprised of the ‘persons’ that founded Bitcoin Of course, you've heard of Bitcoin. You've also heard that its creator is the mysterious Satoshi Nakamoto. This theory emerged on October 31st, 2008, after the Bitcoin white paper was released via a cypherpunk mailing list under the name Satoshi Nakamoto, leaving the world to speculate whether it was an individual or a group. New evidence has brought us closer to identifying who Satoshi is. Many believe Satoshi Nakamoto is just one person; well, we are about to find out. Satoshi Nakamoto Comprises of 3 persons - New Evidence Reveals Recent evidence presented by a Youtuber - Fooch, suggests that Hal Finney, the first person to receive Bitcoin, and Satoshi Nakamoto, the pseudonymous creator of Bitcoin, may have attended the same conference, Crypto '98, in Santa Barbara, California in 1998. By checking the event's program info, Fooch discovers a Satoshi in attendance. Searching for the term "Naka" leads him to a page detailing a presentation by Satoshi Hada and Toshiaki Tanaka on The Existence of 3-round Zero-knowledge Protocols at Crypto '98. The Existence of 3-round Zero-knowledge Protocols Crypto '98. That's not all. Fooch then searched on Google for Satoshi Hada Nakamoto , and boom, this post on X popped up, showing that the creators of Bitcoin allegedly comprise three researchers who may have also worked with IBM. The archived link leads to an IBM research paper. Satoshi Hada Nakamoto “Satoshi is allegedly 3 researchers from IBM”. Fooch said. Also, Fooch’s check on Toshiaki Tanaka and Naohiko Uramoto shows that they, at one time, worked at IBM. Right under the post is a comment tagging what seems to be Satoshi Hada’s account. Here's what it looks like 👇🏽 Maybe a coincidence 🧐 Under Satoshi Hada's most recent post on X, which was in 2017 by the way, someone commented by typing 'Satoshi Nakamoto' backward. What does this person know? Also, Satoshi Hada's previous repost on X in 2012 was about the IBM CEO predicting the future of business. Fooch's intelligent investigation was able to connect the dots based on an interview with Fran, Hal Finney’s wife. She recalled her husband communicating with someone named Satoshi, whom he believed to be based in Japan. Evidence from a YouTube video by The Bitcoin Layer corroborates that Satoshi’s working hours align with the PST time zone which is used in Japan. If “They” Are Satoshi, Why Are They Hiding? Fooch is of the opinion that the Japanese are humble people unlike Americans or Australians who may want to prove anything. Well, what do you think about Fooch’s theory? It appears to be the closest sketch to drawing Satoshi Nakamoto though. Another interesting thing that can be noticed is Satoshi's use of “we” in the bitcoin whitepaper - “We propose a…” Other Alleged Satoshis In an adventure to discover the genius behind Bitcoin, various theories have emerged. Some individuals have purposely claimed to be Satoshi despite evidence proving otherwise. Others have been linked to Satoshi because of their early association with Bitcoin, and one unlucky fellow was accused due to a name coincidence. Let's find out who they are and why they may not be 'Satoshi’. Craig Wright Craig Wright Craig Wright An Australian scientist who has been ruled as a fraudster by a judge in the United Kingdom has long claimed to be the founder of Bitcoin. However, his claims lack tangible evidence beyond document forgery, and he has offered many excuses for his inability to prove it, including 'stomping on the hard drive’ that contains files to prove that he created Bitcoin. Dorian Nakamoto Dorian Nakamoto Dorian Nakamoto When you google Satoshi Nakamoto, the image you often see is that of Dorian Nakamoto, who has denied being the founder of Bitcoin despite the name coincidence. Dorian is a retired physicist. In a 2014 exposé with Newsweek , where he was accused of being the founder of Bitcoin, he claimed he was wrongly quoted when asked about Bitcoin, thinking the question related to his career, and said: Newsweek “I am no longer involved in that and I cannot discuss it.” “I am no longer involved in that and I cannot discuss it.” When the exposé went viral, Dorian stated that he felt victimized and decided to tell his own side of the story. “I’m saying I’m no longer in engineering. That’s it. And even if I was, when we get hired, you have to sign this document, contract saying you will not reveal anything we divulge during and after employment. So that’s what I implied. It sounded like I was involved before with bitcoin and looked like I’m not involved now. That’s not what I meant. I want to clarify that.” “I’m saying I’m no longer in engineering. That’s it. And even if I was, when we get hired, you have to sign this document, contract saying you will not reveal anything we divulge during and after employment. So that’s what I implied. It sounded like I was involved before with bitcoin and looked like I’m not involved now. That’s not what I meant. I want to clarify that.” Dorian's side of the story attracted sympathy from the Bitcoin community, resulting in donations of over 102 bitcoins which is worth almost a hundred million dollars today. Gavin Andersen Gavin Andersen Gavin Andersen It's understandable why some might have suspected Gavin Andresen of being Satoshi Nakamoto. He joined Bitcoin as a core developer in its early stages, creating important features that remain crucial to the Bitcoin ecosystem today. In the documentary "The Bitcoin Story," Gavin repeatedly referred to Satoshi as a "he" and emphasized Satoshi's genius: “He's obviously had some academic training because he wrote this academic whitepaper that is in the style of a peer reviewed academic journal article. He's obviously a genius. I’d love to meet him someday.” “He's obviously had some academic training because he wrote this academic whitepaper that is in the style of a peer reviewed academic journal article. He's obviously a genius. I’d love to meet him someday.” “As far as I know, no one has actually met Satoshi. No one knows who he, or she, or they are. I've never talked with Satoshi although I have communicated with him electronically via email or on online forums. Satoshi started working on Bitcoin he says in 2007, and it actually took him a couple of years to convince himself that he could actually create a system that would work.” “As far as I know, no one has actually met Satoshi. No one knows who he, or she, or they are. I've never talked with Satoshi although I have communicated with him electronically via email or on online forums. Satoshi started working on Bitcoin he says in 2007, and it actually took him a couple of years to convince himself that he could actually create a system that would work.” Gavin believes he was the last person to speak with Satoshi: “I don't know who might have had the last interaction with Satoshi, it might have possibly been me. My very last email to Satoshi, I actually told him I had been invited to give a talk at the CIA. He had already told me he was going to step back. I don't know if me telling him I was going to visit the CIA was the reason I never heard from him again but I certainly think it didn't help.” “I don't know who might have had the last interaction with Satoshi, it might have possibly been me. My very last email to Satoshi, I actually told him I had been invited to give a talk at the CIA. He had already told me he was going to step back. I don't know if me telling him I was going to visit the CIA was the reason I never heard from him again but I certainly think it didn't help.” Hal Finney Hal Finney Hal Finney If you're a Bitcoin enthusiast, you've likely heard of Hal Finney. He was the first person to receive Bitcoin in a transaction, receiving 10 bitcoins from Satoshi Nakamoto on January 12, 2009. This marked him as an early supporter and contributor to the project. As a cryptographer, Finney recognized the potential of Bitcoin, predicted that it would hit $1 million, and actively contributed to its development. He was a passionate advocate for privacy and saw Bitcoin as a solution to financial transaction privacy concerns. Sadly, Hal passed away on August 28, 2014, after battling with complications from Amyotrophic Lateral Sclerosis (ALS) for five years. Recent research by Jameson Lopp, a Bitcoin researcher, has provided evidence that Hal Finney was not Satoshi Nakamoto. This research analyzed the timing of a specific Bitcoin transaction and Hal Finney's participation in a race, concluding that they occurred simultaneously, making it impossible for him to have been Satoshi at that time. Adam Back Adam Back Adam Back British cryptographer and CEO of Blockstream has also been suspected to be Satoshi because of his early involvement with Bitcoin. In 1997, Adam developed Hashcash, a proof-of-work system that is now used in Bitcoin mining. This invention is referenced in Satoshi Nakamoto's Bitcoin white paper. Through public emails revealed between Satoshi and Adam, it appears both individuals have distinct communication styles and technical expertise. Len Sasseman Len Sasseman Len Sasseman Len Sassaman, a renowned cryptographer and academic researcher, made significant contributions to the field of cryptography and privacy. While his work indirectly influenced the development of Bitcoin, there is no concrete evidence to support the claim that he was Satoshi Nakamoto, the pseudonymous creator of Bitcoin. After Sassaman's untimely death in 2011, the Bitcoin community honored him in a unique and lasting way. A tribute was embedded in block 138725 of the Bitcoin blockchain, forever commemorating his legacy. A message in the block reads: " Len was our friend.A brilliant mind, a kind soul, and a devious schemer .” Len was our friend.A brilliant mind, a kind soul, and a devious schemer

Has Satoshi Nakamoto's Identity Finally Been Revealed?

সাতোশি নাকামোটোর পরিচয় কি অবশেষে প্রকাশ করা হয়েছে?

Wurde die Identität von Satoshi Nakamoto endlich enthüllt?

¿Se ha revelado finalmente la identidad de Satoshi Nakamoto?

L'identité de Satoshi Nakamoto a-t-elle enfin été révélée ?

क्या सातोशी नाकामोतो की पहचान अंततः उजागर हो गई है?

サトシ・ナカモトの正体はついに明らかになったのか？

나카모토 사토시의 정체가 드디어 밝혀졌나요?

A identidade de Satoshi Nakamoto foi finalmente revelada?

Личность Сатоши Накамото наконец раскрыта?

Satoshi Nakamoto'nun Kimliği Nihayet Ortaya Çıktı mı?

Danh tính của Satoshi Nakamoto cuối cùng đã được tiết lộ?

中本聪的身份终于揭晓了吗？

Authors:
(1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: shadabahamed1996@gmail.com);
(2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA;
(3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada;
(4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea;
(5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada;
(6) Don Wilson, BC Cancer, Vancouver, BC, Canada;
(7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada;
(8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada;
(9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada;
(10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA;
(11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA;
(12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA;
(13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada;
(14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada. Table of Links Abstract and Intro
Related Work
Materials and Methods
Results
Discussion
Conclusion and References VI. CONCLUSION In this study, we assessed various neural network architectures for automating lymphoma lesion segmentation in PET/CT images across multiple datasets. We examined the reproducibility of lesion measures, revealing differences among networks, highlighting their suitability for specific clinical uses. Additionally, we introduced three lesion detection criteria to assess network performance at a per-lesion level, emphasizing their clinical relevance. Lastly, we discussed challenges related to ground truth consistency and stressed the importance of having well-defined protocol for segmentation. This work provides valuable insights into deep learning’s potentials and limitations in lymphoma lesion segmentation and emphasizes the need for standardized annotation practices to enhance research validity and clinical applications. REFERENCES [1] S. F. Barrington et al. “FDG PET for therapy monitoring in Hodgkin and non-Hodgkin lymphomas”. In: European Journal of Nuclear Medicine and Molecular Imaging 44.1 (Aug. 2017), pp. 97–110. ISSN: 1619- 7089. [2] K. Okuyucu et al. “Prognosis estimation under the light of metabolic tumor parameters on initial FDG-PET/CT in patients with primary extranodal lymphoma”. en. In: Radiol. Oncol. 50.4 (Dec. 2016), pp. 360–369. [3] N. Wu et al. “Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening”. In: IEEE Transactions on Medical Imaging 39.4 (2020), pp. 1184–1194. [4] C. Yuan et al. “Diffuse large B-cell lymphoma segmentation in PET-CT images via hybrid learning for feature fusion”. In: Medical Physics 48.7 (2021), pp. 3665– 3678. [5] H. Hu et al. “Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy”. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020, pp. 1197–1200. [6] H. Li et al. “DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images”. In: IEEE Access 8 (2020), pp. 8004–8018. [7] L. Liu et al. “Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans”. In: Multiscale Multimodal Medical Imaging. Ed. by X. Li et al. Cham: Springer Nature Switzerland, 2022, pp. 121–129. ISBN: 978-3-031-18814-5. [8] C. S. Constantino et al. “Evaluation of Semiautomatic and Deep Learning–Based Fully Automatic Segmentation Methods on [18F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization”. In: Journal of Digital Imaging 36.4 (Aug. 2023), pp. 1864–1876. ISSN: 1618-727X. [9] A. J. Weisman et al. “Comparison of 11 automated PET segmentation methods in lymphoma”. English. In: Physics in medicine & biology 65.23 (2020), pp. 235019–235019. [10] A. J. Weisman et al. “Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma”. In: Radiology: Artificial Intelligence 2.5 (2020), e200016. [11] C. Jiang et al. “Deep learning–based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images”. In: European Radiology 32.7 (July 2022), pp. 4801–4812. ISSN: 1432-1084. [12] P. Blanc-Durand et al. “Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDGPET/CT for total metabolic tumour volume prediction using a convolutional neural network.” In: European Journal of Nuclear Medicine and Molecular Imaging 48.5 (May 2021), pp. 1362–1370. ISSN: 1619-7089. [13] S. Ahamed et al. “A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors and Lymph Nodes from Head and Neck Cancer PET/CT Images”. In: Head and Neck Tumor Segmentation and Outcome Prediction. Ed. by V. Andrearczyk et al. Cham: Springer Nature Switzerland, 2023, pp. 94–106. ISBN: 978-3-031-27420-6. [14] S. Gatidis et al. “A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions”. In: Scientific Data 9.1 (Oct. 2022), p. 601. ISSN: 2052-4463. [15] M. Pop et al. “Left-Ventricle Quantification Using Residual U-Net”. English. In: vol. 11395. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Switzerland: Springer International Publishing AG, 2019, pp. 371–380. ISBN: 0302-9743. [16] A. Myronenko. “3D MRI Brain Tumor Segmentation Using Autoencoder Regularization”. English. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer International Publishing, 2019, pp. 311–320. ISBN: 0302-9743. [17] F. Isensee et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation”. In: Nature Methods 18.2 (Dec. 2020), pp. 203–211. [18] A. Hatamizadeh et al. “Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images”. English. In: (2022). [19] M. J. Cardoso et al. MONAI: An open-source framework for deep learning in healthcare. 2022. arXiv: 2211. 02701 [cs.LG]. [20] S. Ahamed et al. “Towards enhanced lesion segmentation using a 3D neural network trained on multiresolution cropped patches of lymphoma PET images”. In: Journal of Nuclear Medicine 64.supplement 1 (2023), P1360–P1360. ISSN: 0161-5505. [21] J. L. Fleiss. “Measuring nominal scale agreement among many raters”. English. In: Psychological bulletin 76.5 (1971), pp. 378–382. [22] A. K. Jha et al. “Nuclear Medicine and artificial intelligence: Best practices for evaluation (the RELAINCE guidelines)”. en. In: J. Nucl. Med. 63.9 (Sept. 2022), pp. 1288–1299. [23] N. Hasani et al. “Artificial Intelligence in Lymphoma PET Imaging: A Scoping Review (Current Trends and Future Directions)”. In: PET clinics 17.1 (Jan. 1), pp. 145–174. ISSN: 1556-8598. [24] S. K. Warfield et al. “Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation”. en. In: IEEE Trans. Med. Imaging 23.7 (July 2004), pp. 903–921. This paper is available on arxiv under CC 4.0 license. Authors: (1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: shadabahamed1996@gmail.com); (2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA; (3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada; (4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea; (5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada; (6) Don Wilson, BC Cancer, Vancouver, BC, Canada; (7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada; (8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada; (9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada; (10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA; (11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA; (12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA; (13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada; (14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada. Authors: Authors: (1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: shadabahamed1996@gmail.com); (2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA; (3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada; (4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea; (5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada; (6) Don Wilson, BC Cancer, Vancouver, BC, Canada; (7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada; (8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada; (9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada; (10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA; (11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA; (12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA; (13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada; (14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada. Table of Links Abstract and Intro Related Work Materials and Methods Results Discussion Conclusion and References Abstract and Intro Abstract and Intro Related Work Related Work Materials and Methods Materials and Methods Results Results Discussion Discussion Conclusion and References Conclusion and References VI. CONCLUSION In this study, we assessed various neural network architectures for automating lymphoma lesion segmentation in PET/CT images across multiple datasets. We examined the reproducibility of lesion measures, revealing differences among networks, highlighting their suitability for specific clinical uses. Additionally, we introduced three lesion detection criteria to assess network performance at a per-lesion level, emphasizing their clinical relevance. Lastly, we discussed challenges related to ground truth consistency and stressed the importance of having well-defined protocol for segmentation. This work provides valuable insights into deep learning’s potentials and limitations in lymphoma lesion segmentation and emphasizes the need for standardized annotation practices to enhance research validity and clinical applications. REFERENCES [1] S. F. Barrington et al. “FDG PET for therapy monitoring in Hodgkin and non-Hodgkin lymphomas”. In: European Journal of Nuclear Medicine and Molecular Imaging 44.1 (Aug. 2017), pp. 97–110. ISSN: 1619- 7089. [2] K. Okuyucu et al. “Prognosis estimation under the light of metabolic tumor parameters on initial FDG-PET/CT in patients with primary extranodal lymphoma”. en. In: Radiol. Oncol. 50.4 (Dec. 2016), pp. 360–369. [3] N. Wu et al. “Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening”. In: IEEE Transactions on Medical Imaging 39.4 (2020), pp. 1184–1194. [4] C. Yuan et al. “Diffuse large B-cell lymphoma segmentation in PET-CT images via hybrid learning for feature fusion”. In: Medical Physics 48.7 (2021), pp. 3665– 3678. [5] H. Hu et al. “Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy”. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020, pp. 1197–1200. [6] H. Li et al. “DenseX-Net: An End-to-End Model for Lymphoma Segmentation in Whole-Body PET/CT Images”. In: IEEE Access 8 (2020), pp. 8004–8018. [7] L. Liu et al. “Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans”. In: Multiscale Multimodal Medical Imaging. Ed. by X. Li et al. Cham: Springer Nature Switzerland, 2022, pp. 121–129. ISBN: 978-3-031-18814-5. [8] C. S. Constantino et al. “Evaluation of Semiautomatic and Deep Learning–Based Fully Automatic Segmentation Methods on [18F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization”. In: Journal of Digital Imaging 36.4 (Aug. 2023), pp. 1864–1876. ISSN: 1618-727X. [9] A. J. Weisman et al. “Comparison of 11 automated PET segmentation methods in lymphoma”. English. In: Physics in medicine & biology 65.23 (2020), pp. 235019–235019. [10] A. J. Weisman et al. “Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma”. In: Radiology: Artificial Intelligence 2.5 (2020), e200016. [11] C. Jiang et al. “Deep learning–based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images”. In: European Radiology 32.7 (July 2022), pp. 4801–4812. ISSN: 1432-1084. [12] P. Blanc-Durand et al. “Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDGPET/CT for total metabolic tumour volume prediction using a convolutional neural network.” In: European Journal of Nuclear Medicine and Molecular Imaging 48.5 (May 2021), pp. 1362–1370. ISSN: 1619-7089. [13] S. Ahamed et al. “A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors and Lymph Nodes from Head and Neck Cancer PET/CT Images”. In: Head and Neck Tumor Segmentation and Outcome Prediction. Ed. by V. Andrearczyk et al. Cham: Springer Nature Switzerland, 2023, pp. 94–106. ISBN: 978-3-031-27420-6. [14] S. Gatidis et al. “A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions”. In: Scientific Data 9.1 (Oct. 2022), p. 601. ISSN: 2052-4463. [15] M. Pop et al. “Left-Ventricle Quantification Using Residual U-Net”. English. In: vol. 11395. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Switzerland: Springer International Publishing AG, 2019, pp. 371–380. ISBN: 0302-9743. [16] A. Myronenko. “3D MRI Brain Tumor Segmentation Using Autoencoder Regularization”. English. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer International Publishing, 2019, pp. 311–320. ISBN: 0302-9743. [17] F. Isensee et al. “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation”. In: Nature Methods 18.2 (Dec. 2020), pp. 203–211. [18] A. Hatamizadeh et al. “Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images”. English. In: (2022). [19] M. J. Cardoso et al. MONAI: An open-source framework for deep learning in healthcare. 2022. arXiv: 2211. 02701 [cs.LG]. [20] S. Ahamed et al. “Towards enhanced lesion segmentation using a 3D neural network trained on multiresolution cropped patches of lymphoma PET images”. In: Journal of Nuclear Medicine 64.supplement 1 (2023), P1360–P1360. ISSN: 0161-5505. [21] J. L. Fleiss. “Measuring nominal scale agreement among many raters”. English. In: Psychological bulletin 76.5 (1971), pp. 378–382. [22] A. K. Jha et al. “Nuclear Medicine and artificial intelligence: Best practices for evaluation (the RELAINCE guidelines)”. en. In: J. Nucl. Med. 63.9 (Sept. 2022), pp. 1288–1299. [23] N. Hasani et al. “Artificial Intelligence in Lymphoma PET Imaging: A Scoping Review (Current Trends and Future Directions)”. In: PET clinics 17.1 (Jan. 1), pp. 145–174. ISSN: 1556-8598. [24] S. K. Warfield et al. “Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation”. en. In: IEEE Trans. Med. Imaging 23.7 (July 2004), pp. 903–921. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv available on arxiv

Deep Neural Networks to Detect and Quantify Lymphoma Lesions: Conclusion and References

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Tiefe neuronale Netzwerke zur Erkennung und Quantifizierung von Lymphomläsionen: Schlussfolgerung und Referenzen

Redes neuronales profundas para detectar y cuantificar lesiones de linfoma: conclusiones y referencias

Réseaux de neurones profonds pour détecter et quantifier les lésions de lymphome : conclusion et références

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Redes Neurais Profundas para Detectar e Quantificar Lesões de Linfoma: Conclusão e Referências

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