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Towards a general-purpose foundation model for computational pathology.

Richard J Chen^1,2,3,4,5, Tong Ding^1,6, Ming Y Lu^1,2,3,4,7

¹Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Nature Medicine

|March 20, 2024

Summary

This summary is machine-generated.

UNI, a self-supervised model, enhances computational pathology (CPath) by learning from over 100 million tissue images. It achieves state-of-the-art performance on diverse CPath tasks, enabling data-efficient AI for anatomic pathology.

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A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Area of Science:

Computational pathology
Digital pathology
Artificial intelligence in medicine

Background:

Quantitative evaluation of histopathology images is vital for computational pathology (CPath).
High-resolution whole-slide images (WSIs) and feature variability pose annotation challenges for AI.
Existing transfer learning and self-supervised methods lack large-scale, diverse evaluation across tissue types.

Purpose of the Study:

Introduce UNI, a general-purpose self-supervised model for pathology.
Pretrain UNI on a massive dataset of H&E-stained WSIs for broad applicability.
Evaluate UNI's performance and novel capabilities on a wide range of CPath tasks.

Main Methods:

Developed UNI, a self-supervised model for pathology.
Pretrained UNI on over 100 million H&E-stained WSIs (>77 TB) from 20 tissue types.
Evaluated UNI on 34 diverse CPath tasks, including classification and subtyping.

Main Results:

UNI outperformed previous state-of-the-art models on CPath tasks.
Demonstrated resolution-agnostic tissue classification capabilities.
Achieved strong performance in few-shot slide classification and disease subtyping (108 cancer types).

Conclusions:

UNI represents a significant advancement in large-scale unsupervised representation learning for CPath.
The model enables data-efficient AI models that generalize across diverse tasks and clinical workflows.
UNI facilitates the development of robust AI tools for anatomic pathology.