Multistain Pretraining for Slide Representation Learning in Pathology

Title: Multistain Pretraining for Slide Representation Learning in Pathology

Authors: Guillaume Jaume, Anurag Vaidya, Andrew Zhang, Andrew H. Song, Richard J. Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, Faisal Mahmood

Published: Aug 5 2024

Link: https://arxiv.org/abs/2408.02859

Summary:

• Authors proposed a multimodal pretraining strategy called “MADELEINE”, a slide representation learning method, leveraging multiple stained slides images for richer training.
• Trained MADELEINE encoder can be used for some downstream tasks such as few-shot classification, prognostication and fine-tuning.
• Training process utilizes a dual global-local cross-stain alignment objective with breast cancer samples and kidney transplant samples.

Summary (Generated by Microsoft Copilot):

Introduction:

• The paper introduces Madeleine, a multimodal pretraining strategy for slide representation learning in computational pathology.

Challenges:

• Existing methods are limited by the clinical and biological diversity of views and the scale of whole-slide images (WSIs).

Methods:

• Madeleine uses a dual global-local cross-stain alignment objective on large cohorts of breast and kidney samples.

Novelties:

• Introduces a multimodal pretraining strategy leveraging multiple stains as different views to form a rich training signal.

Results:

• Demonstrates the quality of slide representations on various downstream evaluations, including morphological and molecular classification.

Performances:

• Madeleine outperforms existing methods in few-shot classification and full classification tasks.

Limitations:

• The study does not involve datasets used for downstream tasks, precluding any data leakage.

Discussion:

• Highlights the importance of using clinically and biologically meaningful views provided by multimodal pretraining.