Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
Karin Stacke1, Jonas Unger2, Claes Lundström3, Gabriel Eilertsen2
1: Department of Science and Technology (ITN), Linköping University, Sectra AB, Sweden, 2: Department of Science and Technology (ITN), Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden, 3: Department of Science and Technology (ITN), Center for Medical Image Science and Visualization (CMIV), Linköping University, Sectra AB, Sweden
August 2022 issue
Publication date: 2022/08/18
Abstract
Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking self-supervised learning has been ImageNet, for which recent methods are approaching the performance achieved by fully supervised training. The ImageNet dataset is however largely object-centric, and it is not clear yet what potential those methods have on widely different datasets and tasks that are not object-centric, such as in digital pathology. While self-supervised learning has started to be explored within this area with encouraging results, there is reason to look closer at how this setting differs from natural images and ImageNet. In this paper we make an in-depth analysis of contrastive learning for histopathology, pin-pointing how the contrastive objective will behave differently due to the characteristics of histopathology data. Using SimCLR and H&E stained images as a representative setting for contrastive self-supervised learning in histopathology, we bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning. In a large battery of experiments, we analyze how the downstream performance in tissue classification will be affected by these considerations. The results point to how contrastive learning can reduce the annotation effort within digital pathology, but that the specific dataset characteristics need to be considered. To take full advantage of the contrastive learning objective, different calibrations of view generation and hyper-parameters are required. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications. Code and trained models are available at https://github.com/k-stacke/ssl-pathology
Keywords
Deep Learning · Histopathology · Self-Supervised Learning · Transfer learning · Contrastive Learning · H&E · Whole-slide image analysi
Bibtex
@article{melba:2022:023:stacke,
title = "Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications",
author = "Stacke, Karin and Unger, Jonas and Lundström, Claes and Eilertsen, Gabriel",
journal = "Machine Learning for Biomedical Imaging",
volume = "1",
issue = "August 2022 issue",
year = "2022",
pages = "1--33",
issn = "2766-905X",
url = "https://melba-journal.org/2022:023"
}
RIS
TY - JOUR
AU - Stacke, Karin
AU - Unger, Jonas
AU - Lundström, Claes
AU - Eilertsen, Gabriel
PY - 2022
TI - Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
T2 - Machine Learning for Biomedical Imaging
VL - 1
IS - August 2022 issue
SP - 1
EP - 33
SN - 2766-905X
UR - https://melba-journal.org/2022:023
ER -
