Table of Links Abstract and I. Introduction Abstract and I. Introduction Abstract and I. Introduction Materials and Methods
2.1. Multiple Instance Learning
2.2. Model Architectures


Results
3.1. Training Methods
3.2. Datasets
3.3. WSI Preprocessing Pipeline
3.4. Classification and RoI Detection Results


Discussion
4.1. Tumor Detection Task
4.2. Gene Mutation Detection Task


Conclusions


Acknowledgements


Author Declaration and References Materials and Methods
2.1. Multiple Instance Learning
2.2. Model Architectures Materials and Methods Materials and Methods Materials and Methods 2.1. Multiple Instance Learning 2.1. Multiple Instance Learning 2.2. Model Architectures 2.2. Model Architectures Results
3.1. Training Methods
3.2. Datasets
3.3. WSI Preprocessing Pipeline
3.4. Classification and RoI Detection Results Results Results 3.1. Training Methods 3.1. Training Methods 3.2. Datasets 3.2. Datasets 3.3. WSI Preprocessing Pipeline 3.3. WSI Preprocessing Pipeline 3.4. Classification and RoI Detection Results 3.4. Classification and RoI Detection Results Discussion
4.1. Tumor Detection Task
4.2. Gene Mutation Detection Task Discussion Discussion 4.1. Tumor Detection Task 4.1. Tumor Detection Task 4.2. Gene Mutation Detection Task 4.2. Gene Mutation Detection Task Conclusions Conclusions Conclusions Conclusions Acknowledgements Acknowledgements Acknowledgements Acknowledgements Author Declaration and References Author Declaration and References Author Declaration and References Author Declaration and References 3.4. Classification and RoI Detection Results In this section, we present the results obtained for our classification tasks, as well as some examples of the RoIs produced by the models. After fine-tuning, we performed, for each model, task, and magnification level, five independent runs where the datasets were randomly split with different seeds. Tables 1 and 2 display the average and standard deviation of the Areas under the Curves (AUCs) obtained on the test set for those five runs. We also present graphs displaying the ROC curves of one of these runs for each model, task and magnification. Table 1 shows the AUC results for the tumor detection task at magnification 5x. Figure 7 shows an example of the heatmaps produced for the tumor detection task in a slide at the same magnification level. Table 2 presents the gene mutation detection task AUC results at magnification levels 5x, 10x and 20x. Figures 8 and 9 present examples of heatmaps produced for the gene mutation detection task from patches of magnification 10x and 20x. Due to the poor classification results obtained at magnification 5x, we did not include heatmaps from this level, since we concluded that they would not be meaningful. For the AMIL model we only have the attention scores, corresponding to the patches that were considered the most relevant for the final prediction. Regarding the original AdMIL and our version, we have the patch attention scores produced by the attention layer, as well as the excitatory and inhibitory patch scores, that indicate positive and negative contributions for the final prediction, respectively. These final scores were passed through a sigmoid to scale the logits to values between 0 and 1, where values in the interval ]0, 0.5[ indicates a negative contribution and values in [0.5, 1[ a positive contribution. In the case of attention heatmaps, we show a continuous colormap. For the inhibitory/excitatory scores, we only use two colors, one for the excitatory patches (Red) and other for the inhibitory patches (Blue). Authors:
(1) Martim Afonso, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon, 1049-001, Portugal;
(2) Praphulla M.S. Bhawsar, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20850, Maryland, USA;
(3) Monjoy Saha, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20850, Maryland, USA;
(4) Jonas S. Almeida, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20850, Maryland, USA;
(5) Arlindo L. Oliveira, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon, 1049-001, Portugal and INESC-ID, R. Alves Redol 9, Lisbon, 1000-029, Portugal. Authors: Authors: (1) Martim Afonso, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon, 1049-001, Portugal; (2) Praphulla M.S. Bhawsar, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20850, Maryland, USA; (3) Monjoy Saha, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20850, Maryland, USA; (4) Jonas S. Almeida, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, 20850, Maryland, USA; (5) Arlindo L. Oliveira, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon, 1049-001, Portugal and INESC-ID, R. Alves Redol 9, Lisbon, 1000-029, Portugal. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Heatmap and Accuracy Results from Medical Image Classification Models

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Coarse-to-Fine Approach to Text-Guided 3D Localization Without Ground Truth

How AI Detects Cancer in Whole Slide Images

A Comparative Study of Attention-Based MIL Architectures in Cancer Detection

How We Trained AI Models to Detect Tumors and Gene Mutations

How Researchers Are Preprocessing Gigabyte-Sized WSIs for Deep Learning

How AI Models Are Rethinking Tumor Detection at the Tissue Level

A Coarse-to-Fine Approach to Text-Guided 3D Localization Without Ground Truth

How AI Detects Cancer in Whole Slide Images

A Comparative Study of Attention-Based MIL Architectures in Cancer Detection

How We Trained AI Models to Detect Tumors and Gene Mutations

How Researchers Are Preprocessing Gigabyte-Sized WSIs for Deep Learning

How AI Models Are Rethinking Tumor Detection at the Tissue Level

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps