Contrastive Learning (CL) is a recent representation learning approach, which encourages inter-class separability and intra-class compactness in learned image representations. Since medical images often contain multiple semantic classes in an image, using CL to learn representations of local features (as opposed to global) is important. In this work, we present a novel semi-supervised 2D medical segmentation solution that applies CL on image patches, instead of full images. These patches are meaningfully constructed using the semantic information of different classes obtained via pseudo labeling. We also propose a novel consistency regularization (CR) scheme, which works in synergy with CL. It addresses the problem of confirmation bias, and encourages better clustering in the feature space. We evaluate our method on four public medical segmentation datasets and a novel histopathology dataset that we introduce. Our method obtains consistent improvements over state-of-the-art semi-supervised segmentation approaches for all datasets.
@article{arxiv.2106.06801,
title = {Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation},
author = {Prashant Pandey and Ajey Pai and Nisarg Bhatt and Prasenjit Das and Govind Makharia and Prathosh AP and Mausam},
journal= {arXiv preprint arXiv:2106.06801},
year = {2021}
}
Comments
The paper is withdrawn due to a bug in experimental protocol that renders its experimental results and observations invalid. All expts were conducted by the student authors. The roles of senior authors (Prasenjit Das, Govind Makharia, Prathosh, and Mausam) were in defining the problem statement, discussions of potential solutions and framing of the paper and not in performing experiments