Rotation-Agnostic Image Representation Learning for Digital Pathology
arXiv (Cornell University)(2023)
摘要
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://rhazeslab.github.io/PathDino-Page/
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