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Unsupervised mutual transformer learning for multi-gigapixel Whole Slide Image classification.

Sajid Javed¹, Arif Mahmood², Talha Qaiser³

¹Department of Computer Science, Khalifa University of Science and Technology, Abu Dhabi, P.O. Box 127788, United Arab Emirates.

Medical Image Analysis

|May 29, 2024

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This summary is machine-generated.

This study introduces an unsupervised deep learning method for classifying gigapixel Whole Slide Images (WSIs). The novel mutual transformer learning approach generates and refines pseudo-labels, improving computational pathology diagnostics without expert annotations.

Area of Science:

Computational Pathology
Digital Pathology
Artificial Intelligence in Medicine

Background:

Whole Slide Image (WSI) classification is crucial in computational pathology for applications like cancer detection.
Current deep learning methods often rely on extensive manual annotations by pathologists, which are costly and time-consuming.
Existing weakly supervised methods still require large, slide-level labeled datasets.

Purpose of the Study:

To develop a fully unsupervised algorithm for Whole Slide Image (WSI) classification.
To eliminate the need for manual annotations by expert pathologists.
To demonstrate the framework's utility in weakly supervised learning and cancer subtype classification.

Main Methods:

Proposes a novel unsupervised WSI classification algorithm utilizing mutual transformer learning.

Keywords:

Cancer imaging Computational pathology Multi-gigapixel Whole Slide Images Unsupervised learning Vision transformer

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Transforms image instances (patches) into a latent space and back, using transformation loss to generate pseudo-labels.

Employs a transformer-based label cleaner and a discriminative learning mechanism for improved instance labeling.

Main Results:

Achieved superior performance compared to state-of-the-art methods on four public datasets.
Demonstrated the effectiveness of the unsupervised framework for downstream weakly supervised tasks.
Successfully applied the method to cancer subtype classification.

Conclusions:

The proposed mutual transformer learning framework offers a powerful unsupervised approach for WSI classification.
This method significantly reduces the reliance on manual expert annotations in computational pathology.
The algorithm shows promise for advancing automated diagnostics and subtype classification in digital pathology.