Towards a Visual-Language Foundation Model for Computational Pathology

Title: Towards a Visual-Language Foundation Model for Computational Pathology

Authors: Ming Y. Lu, Bowen Chen, Drew F. K. Williamson, Richard J. Chen, Ivy Liang, Tong Ding, Guillaume Jaume, Igor Odintsov, Andrew Zhang, Long Phi Le, Georg Gerber, Anil V Parwani, Faisal Mahmood

Published: Jul 24, 2023

Link: https://arxiv.org/abs/2307.12914

Summary (Generated by Microsoft Copilot):

Introduction:

• The paper introduces CONCH, a visual-language foundation model for histopathology, developed using over 1.17 million image-caption pairs.

Challenges:

• Label scarcity in medical domains and the labor-intensive process of data collection and annotation.

Methods:

• Utilizes contrastive learning and captioning objectives for pretraining, leveraging diverse histopathology images and biomedical text.

Novelties:

• CONCH’s ability to perform zero-shot classification, segmentation, and retrieval tasks without task-specific training data.

Results:

• Achieves state-of-the-art performance across 13 diverse benchmarks, including histology image classification and segmentation.

Performances:

• Outperforms other models like PLIP and BiomedCLIP in various tasks, often by a significant margin.

Limitations:

• The scale of pretraining data is smaller compared to general visual-language models, limiting zero-shot recognition capabilities.

Discussion:

• Highlights the potential of CONCH to reduce the burden of annotating training examples and improve retrieval capabilities in clinical settings.