Rotation-Agnostic Image Representation Learning for Digital Pathology

• URL: http://arxiv.org/abs/2311.08359v2
• Date: Tue, 12 Mar 2024 16:40:28 GMT
• Title: Rotation-Agnostic Image Representation Learning for Digital Pathology
• Authors: Saghir Alfasly, Abubakr Shafique, Peyman Nejat, Jibran Khan, Areej Alsaafin, Ghazal Alabtah, H.R. Tizhoosh
• Abstract summary: This paper introduces a fast patch selection method, FPS, for whole-slide image (WSI) analysis. It also presents PathDino, a lightweight histopathology feature extractor with a minimal configuration of five Transformer blocks. We show that our compact model outperforms existing state-of-the-art histopathology-specific vision transformers on 12 diverse datasets.
• Score: 0.8246494848934447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses complex challenges in histopathological image analysis through three key contributions. Firstly, it introduces a fast patch selection method, FPS, for whole-slide image (WSI) analysis, significantly reducing computational cost while maintaining accuracy. Secondly, it presents PathDino, a lightweight histopathology feature extractor with a minimal configuration of five Transformer blocks and only 9 million parameters, markedly fewer than alternatives. Thirdly, it introduces a rotation-agnostic representation learning paradigm using self-supervised learning, effectively mitigating overfitting. We also show that our compact model outperforms existing state-of-the-art histopathology-specific vision transformers on 12 diverse datasets, including both internal datasets spanning four sites (breast, liver, skin, and colorectal) and seven public datasets (PANDA, CAMELYON16, BRACS, DigestPath, Kather, PanNuke, and WSSS4LUAD). Notably, even with a training dataset of 6 million histopathology patches from The Cancer Genome Atlas (TCGA), our approach demonstrates an average 8.5% improvement in patch-level majority vote performance. These contributions provide a robust framework for enhancing image analysis in digital pathology, rigorously validated through extensive evaluation. Project Page: https://kimialabmayo.github.io/PathDino-Page/

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