Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised learning, as it generates variations in image samples. However, traditional image augmentation techniques often overlook the unique characteristics of histopathological images. In this paper, we propose a new histopathology-specific image augmentation method called stain reconstruction augmentation (SRA). We integrate our SRA with MoCo v3, a leading model in self-supervised contrastive learning, along with our additional contrastive loss terms, and call the new model SRA-MoCo v3. We demonstrate that our SRA-MoCo v3 always outperforms the standard MoCo v3 across various downstream tasks and achieves comparable or superior performance to other foundation models pre-trained on significantly larger histopathology datasets.
@article{arxiv.2410.17514,
title = {SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images},
author = {Hamid Manoochehri and Bodong Zhang and Beatrice S. Knudsen and Tolga Tasdizen},
journal= {arXiv preprint arXiv:2410.17514},
year = {2024}
}
Comments
Hamid Manoochehri and Bodong Zhang contributed equally to this work