An Exceedingly Simple Consistency Regularization Method For Semi-Supervised Medical Image Segmentation

Hritam Basak, Rajarshi Bhattacharya, Rukhshanda Hussain, Agniv Chatterjee

20222022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)35 citationsDOI

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 un-der 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

Topics & Concepts

OverfittingSegmentationComputer scienceArtificial intelligenceRegularization (linguistics)Image segmentationScale-space segmentationPattern recognition (psychology)Machine learningComputer visionArtificial neural networkAdvanced Neural Network ApplicationsMedical Image Segmentation TechniquesGenerative Adversarial Networks and Image Synthesis