Contrastive Learning (CL) is a recent representation learning approach, which achieves promising results by encouraging inter-class separability and intra-class compactness in learned image representations. Because medical images often contain multiple classes of interest per image, a standard image-level CL for these images is not applicable . 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  scheme, which works in synergy with contrastive learning . It addresses the problem of confirmation bias often observed in semi-supervised settings , and encourages better clustering in the feature space. We evaluate our method on four public medical segmentation datasets along with a novel histopathology dataset that we introduce. Our method obtains consistent improvements over the state-of-the-art semi-supervised segmentation approaches for all datasets.
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.
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classes of interest per image, a standard image-level CL for these images is not applicable
semantic classes in an image, using CL to learn representations of local features (as opposed to global) is important
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often observed in semi-supervised settings
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