Self-Supervised Similarity Learning for Digital Pathology

Jacob Gildenblat, Eldad Klaiman

Keywords: Deep Learning, Self-Supervised, Similarity Learning, Digital Pathology

TL;DR: 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.

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 specific
image information. In digital pathology, supervised training data is expensive
and dicult 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 rst published
method for self-supervised learning tailored for digital pathology.