ArXiv Preprint
Semantic segmentation of various tissue and nuclei types in histology images
is fundamental to many downstream tasks in the area of computational pathology
(CPath). In recent years, Deep Learning (DL) methods have been shown to perform
well on segmentation tasks but DL methods generally require a large amount of
pixel-wise annotated data. Pixel-wise annotation sometimes requires expert's
knowledge and time which is laborious and costly to obtain. In this paper, we
present a consistency based semi-supervised learning (SSL) approach that can
help mitigate this challenge by exploiting a large amount of unlabelled data
for model training thus alleviating the need for a large annotated dataset.
However, SSL models might also be susceptible to changing context and features
perturbations exhibiting poor generalisation due to the limited training data.
We propose an SSL method that learns robust features from both labelled and
unlabelled images by enforcing consistency against varying contexts and feature
perturbations. The proposed method incorporates context-aware consistency by
contrasting pairs of overlapping images in a pixel-wise manner from changing
contexts resulting in robust and context invariant features. We show that
cross-consistency training makes the encoder features invariant to different
perturbations and improves the prediction confidence. Finally, entropy
minimisation is employed to further boost the confidence of the final
prediction maps from unlabelled data. We conduct an extensive set of
experiments on two publicly available large datasets (BCSS and MoNuSeg) and
show superior performance compared to the state-of-the-art methods.
Raja Muhammad Saad Bashir, Talha Qaiser, Shan E Ahmed Raza, Nasir M. Rajpoot
2023-01-30
