# Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology

Source: [Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology](<https://www.sota2.com/research/paper/68fa68e11e209131cea1eeb5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:48:52.480Z

Authors: Olivier Dehaene, Axel Camara, Olivier Moindrot, Axel de Lavergne, Pierre Courtiol

Published: 2020-12-07T10:59:38.000Z

## Abstract

One of the biggest challenges for applying machine learning to histopathology is weak supervision: whole-slide images have billions of pixels yet often only one global label. The state of the art therefore relies on strongly-supervised model training using additional local annotations from domain experts. However, in the absence of detailed annotations, most weakly-supervised approaches depend on a frozen feature extractor pre-trained on ImageNet. We identify this as a key weakness and propose to train an in-domain feature extractor on histology images using MoCo v2, a recent self-supervised learning algorithm. Experimental results on Camelyon16 and TCGA show that the proposed extractor greatly outperforms its ImageNet counterpart. In particular, our results improve the weakly-supervised state of the art on Camelyon16 from 91.4% to 98.7% AUC, thereby closing the gap with strongly-supervised models that reach 99.3% AUC. Through these experiments, we demonstrate that feature extractors trained via self-supervised learning can act as drop-in replacements to significantly improve existing machine learning techniques in histology. Lastly, we show that the learned embedding space exhibits biologically meaningful separation of tissue structures.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2012.03583>)

[PDF](<https://arxiv.org/pdf/2012.03583.pdf>)