DeepAI AI Chat
Log In Sign Up

Self-Supervised Similarity Learning for Digital Pathology

by   Jacob Gildenblat, et al.

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 whole slide images (WSI) 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.


page 5

page 8


Self supervised contrastive learning for digital histopathology

Unsupervised learning has been a long-standing goal of machine learning ...

Self-supervised Product Quantization for Deep Unsupervised Image Retrieval

Supervised deep learning-based hash and vector quantization are enabling...

Self-EMD: Self-Supervised Object Detection without ImageNet

In this paper, we propose a novel self-supervised representation learnin...

Learning an Adaptation Function to Assess Image Visual Similarities

Human perception is routinely assessing the similarity between images, b...

Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers

Microsatellite instability (MSI) is a tumor phenotype whose diagnosis la...

Exploring the Limits of Deep Image Clustering using Pretrained Models

We present a general methodology that learns to classify images without ...

EventPoint: Self-Supervised Local Descriptor Learning for Event Cameras

We proposes a method of extracting intrest points and descriptors using ...