An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2202.00677v2
• Date: Thu, 3 Feb 2022 08:27:08 GMT
• Title: An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation
• Authors: Hritam Basak, Rajarshi Bhattacharya, Rukhshanda Hussain, Agniv Chatterjee
• Abstract summary: The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. We introduce a novel regularization strategy involving computation-based mixing for semi-supervised medical image segmentation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. In this paper, we introduce a novel regularization strategy involving interpolation-based mixing for semi-supervised medical image segmentation. The proposed method is a new consistency regularization strategy that encourages segmentation of interpolation of two unlabelled data to be consistent with the interpolation of segmentation maps of those data. This method represents a specific type of data-adaptive regularization paradigm which aids to minimize the overfitting of labelled data under high confidence values. The proposed method is advantageous over adversarial and generative models as it requires no additional computation. Upon evaluation on two publicly available MRI datasets: ACDC and MMWHS, experimental results demonstrate the superiority of the proposed method in comparison to existing semi-supervised models. Code is available at: https://github.com/hritam-98/ICT-MedSeg

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