Self-Supervised Similarity Learning for Digital Pathology

TL;DR

This paper introduces a simple self-supervised similarity learning method for digital pathology that produces compact, robust image descriptors, outperforming existing ImageNet-based features in tumor tile retrieval tasks.

Contribution

The paper presents the first self-supervised learning approach specifically designed for digital pathology, using spatial continuity in WSIs to improve feature extraction.

Findings

Outperforms existing ImageNet-based features in retrieval tasks

Produces 128-dimensional descriptors with lower memory and faster processing

First method tailored for self-supervised learning in digital pathology

Abstract

Using features extracted from networks pretrained on ImageNet is a common practice in applications of deep learning for digital pathology. However it presents the downside of missing domain specific image information. In digital pathology, supervised training data is expensive and difficult to collect. We propose a self-supervised method for feature extraction by similarity learning on whole slide images (WSI) that is simple to implement and allows creation of robust and compact image descriptors. We train a siamese network, exploiting image spatial continuity and assuming spatially adjacent tiles in the image are more similar to each other than distant tiles. Our network outputs feature vectors of length 128, which allows dramatically lower memory storage and faster processing than networks pretrained on ImageNet. We apply the method on digital pathology WSIs from the Camelyon16 train set and assess and compare our method by measuring image retrieval of tumor tiles and descriptor pair distance ratio for distant/near tiles in the Camelyon16 test set. We show that our method yields better retrieval task results than existing ImageNet based and generic self-supervised feature extraction methods. To the best of our knowledge, this is also the first published method for self-supervised learning tailored for digital pathology.