  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.
