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Updated: May 8, 2026

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Published on: March 29, 2021

A multimodal knowledge-enhanced whole-slide pathology foundation model.

Yingxue Xu¹, Yihui Wang¹, Fengtao Zhou¹

¹Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.

Nature Communications

|December 12, 2025

Summary

This summary is machine-generated.

A new foundation model, mSTAR (Multimodal Self-TAught PRetraining), integrates pathology slides, reports, and gene data for comprehensive cancer analysis. This multimodal approach significantly improves performance over vision-only models in oncological tasks.

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Computational pathology
Artificial intelligence in oncology
Digital pathology

Background:

Foundation models in computational pathology face challenges integrating multimodal data (reports, gene expression) and capturing whole-slide context.
Current models often use vision-only or image-caption data, neglecting crucial insights from pathology reports and gene expression profiles.
Patch-level analysis in existing models limits the capture of comprehensive whole-slide patterns.

Purpose of the Study:

To introduce mSTAR (Multimodal Self-TAught PRetraining), a novel pathology foundation model designed for unified multimodal integration.
To incorporate pathology slides, expert-created reports, and gene expression data within a single framework.
To advance computational pathology by enabling both slide-level analysis and multimodal data fusion.

Main Methods:

Developed mSTAR, a foundation model integrating three modalities: pathology slides, expert-created reports, and gene expression data.
Utilized a dataset of 26,169 slide-level modality pairs across 32 cancer types, including over 116 million patch images.
Implemented a framework to inject multimodal whole-slide context into patch representations, enabling slide-level analysis.

Main Results:

mSTAR demonstrated superior performance across an oncological benchmark of 97 tasks compared to previous state-of-the-art models.
The model showed particular strength in molecular prediction and multimodal tasks.
Results indicate that multimodal integration provides greater performance improvements than simply expanding vision-only datasets.

Conclusions:

mSTAR effectively integrates multimodal data (slides, reports, gene expression) for enhanced computational pathology.
The model advances analysis from patch-level to slide-level and from single to multiple modalities.
Multimodal foundation models represent a significant step forward in cancer diagnostics and research.