Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation
- URL: http://arxiv.org/abs/2106.06801v1
- Date: Sat, 12 Jun 2021 15:43:24 GMT
- Title: Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation
- Authors: Prashant Pandey, Ajey Pai, Nisarg Bhatt, Prasenjit Das, Govind
Makharia, Prathosh AP, Mausam
- Abstract summary: We present a novel semi-supervised 2D medical segmentation solution that applies Contrastive Learning (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.
- Score: 16.517086214275654
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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