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

arXiv: https://arxiv.org/abs/2202.00677 · HTML: https://mlsanity.com/paper/2202.00677/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/1903.00348/) (2019)
- [Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer](https://mlsanity.com/paper/1807.07543/) (2018)
- [Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations](https://mlsanity.com/paper/1911.01218/) (2019)
- [Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization](https://mlsanity.com/paper/2103.04813/) (2021)

## Built on by (most cited first, 10 of 10)

- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI](https://mlsanity.com/paper/2209.00123/) (2022)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/2403.03512/) (2024)
- [Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift](https://mlsanity.com/paper/2210.01891/) (2022)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging](https://mlsanity.com/paper/2304.09327/) (2023)
- [Analysing the effectiveness of a generative model for semi-supervised medical image segmentation](https://mlsanity.com/paper/2211.01886/) (2022)
- [Randomized Dimension Reduction with Statistical Guarantees](https://mlsanity.com/paper/2310.01739/) (2023)
- [SM2C: Boost the Semi-supervised Segmentation for Medical Image by using Meta Pseudo Labels and Mixed Images](https://mlsanity.com/paper/2403.16009/) (2024)

## Abstract (arXiv)

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