Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation

TL;DR

This paper introduces a human-machine interactive approach that leverages self-supervised learning and tissue prototypes to achieve high-quality histopathology image segmentation with minimal annotation effort.

Contribution

It proposes a novel tissue prototype dictionary building pipeline combined with self-supervised contrastive learning for label-efficient segmentation.

Findings

Achieves comparable performance to fully-supervised methods with less annotation.

Outperforms existing weakly-supervised segmentation techniques.

Demonstrates effectiveness on two public datasets.

Abstract

Recently, deep neural networks have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole slide images and pathologists' heavy daily workload, obtaining pixel-level labels for supervised learning in clinical practice is often infeasible. Alternatively, weakly-supervised segmentation methods have been explored with less laborious image-level labels, but their performance is unsatisfactory due to the lack of dense supervision. Inspired by the recent success of self-supervised learning methods, we present a label-efficient tissue prototype dictionary building pipeline and propose to use the obtained prototypes to guide histopathology image segmentation. Particularly, taking advantage of self-supervised contrastive learning, an encoder is trained to project the unlabeled histopathology image patches into a discriminative embedding space where these patches are clustered to identify the tissue prototypes by efficient pathologists' visual examination. Then, the encoder is used to map the images into the embedding space and generate pixel-level pseudo tissue masks by querying the tissue prototype dictionary. Finally, the pseudo masks are used to train a segmentation network with dense supervision for better performance. Experiments on two public datasets demonstrate that our human-machine interactive tissue prototype learning method can achieve comparable segmentation performance as the fully-supervised baselines with less annotation burden and outperform other weakly-supervised methods. Codes will be available upon publication.