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Related Experiment Video

Updated: Jun 23, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Finding Regions of Interest in Whole Slide Images Using Multiple Instance Learning.

Martim Afonso¹, Praphulla M S Bhawsar², Monjoy Saha²

¹Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon, 1049-001, Portugal.

|June 21, 2024

Summary

This summary is machine-generated.

This study uses weakly supervised Multiple Instance Learning (MIL) for analyzing whole slide images in cancer pathology. The AI models accurately predict cancer phenotypes and identify cellular morphologies linked to TP53 mutations.

Keywords:

Attention mechanism Cancer Multiple Instance Learning TP53 Whole Slide Image

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Last Updated: Jun 23, 2025

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Published on: April 8, 2016

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Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Published on: May 3, 2018

Area of Science:

Digital Pathology
Artificial Intelligence
Computational Biology

Background:

Whole Slide Images (WSI) are crucial in Digital Pathology but pose AI analysis challenges due to slide-level annotations.
Pathology diagnostics and oncogene mutation data (e.g., TCGA) are often recorded at the specimen or slide level, not tile level.
This creates a dual challenge: predicting cancer phenotype and linking cellular morphology to it.

Purpose of the Study:

To address challenges in AI analysis of WSI with slide-level labels.
To explore weakly supervised Multiple Instance Learning (MIL) for cancer phenotype prediction and mutation association.
To investigate AI performance in tumor detection and TP53 mutation identification in Invasive Breast Carcinoma and Lung Squamous Cell Carcinoma.

Main Methods:

Applied a weakly supervised Multiple Instance Learning (MIL) approach to Whole Slide Images (WSI).
Utilized datasets from The Cancer Genome Atlas (TCGA) for Invasive Breast Carcinoma (TCGA-BRCA) and Lung Squamous Cell Carcinoma (TCGA-LUSC).
Evaluated tumor detection and TP53 mutation prediction using novel additive MIL and Attention MIL architectures at various magnification levels.

Main Results:

A novel additive MIL implementation achieved high performance (AUC 0.96), comparable to reference methods.
Attention MIL slightly outperformed other methods (AUC 0.97).
Different AI architectures showed distinct sensitivities to morphological features at various magnification levels, with TP53 mutation detection being most sensitive at higher magnifications.

Conclusions:

Weakly supervised MIL is effective for analyzing Whole Slide Images (WSI) in Digital Pathology, even with slide-level annotations.
AI models can predict cancer phenotypes and identify morphology-mutation associations.
The study highlights the importance of magnification levels in AI-driven pathological analysis for molecular insights.