  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
