Papers › An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical...

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

1 Feb 2022arXiv:2202.00677archive 2025-07-28

Hritam Basak, Rajarshi Bhattacharya, Rukhshanda Hussain, Agniv Chatterjee

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

PaperPDFCode

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

hritam-98/ict-medseg officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

3D Medical Imaging SegmentationImage SegmentationMedical Image SegmentationSegmentationSemantic SegmentationSemi-supervised Medical Image Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semi-supervised Medical Image Segmentation MM-WHS 2017 ICT-MedSeg DSC 79.83 #2 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections