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

Understand

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.