# Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations

arXiv: https://arxiv.org/abs/1911.01218 · HTML: https://mlsanity.com/paper/1911.01218/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 30 of 71)

- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2105.07059/) (2021)
- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective](https://mlsanity.com/paper/2302.01735/) (2023)
- [Class-Aware Adversarial Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2201.10737/) (2022)
- [Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels](https://mlsanity.com/paper/2107.13741/) (2021)
- [Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels](https://mlsanity.com/paper/2209.13476/) (2022)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery](https://mlsanity.com/paper/2212.14310/) (2022)
- [Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2206.09293/) (2022)
- [Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures](https://mlsanity.com/paper/2208.08605/) (2022)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation](https://mlsanity.com/paper/2207.09084/) (2022)
- [Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2303.13090/) (2023)
- [Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization](https://mlsanity.com/paper/2103.04813/) (2021)
- [Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework](https://mlsanity.com/paper/2212.10305/) (2022)
- [Anatomically-aware Uncertainty for Semi-supervised Image Segmentation](https://mlsanity.com/paper/2310.16099/) (2023)
- [Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations](https://mlsanity.com/paper/2110.11998/) (2021)
- [Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2111.10989/) (2021)
- [An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2202.00677/) (2022)
- [Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation](https://mlsanity.com/paper/2210.10956/) (2022)
- [Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction](https://mlsanity.com/paper/2105.14773/) (2021)
- [Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis](https://mlsanity.com/paper/2407.07295/) (2024)
- [Segmentation with mixed supervision: Confidence maximization helps knowledge distillation](https://mlsanity.com/paper/2109.10902/) (2021)
- [FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.00400/) (2022)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation](https://mlsanity.com/paper/2203.05682/) (2022)
- [Uncertainty-Aware Self-supervised Neural Network for Liver T1ρ Mapping with Relaxation Constraint](https://mlsanity.com/paper/2207.03105/) (2022)

## Abstract (arXiv)

The scarcity of labeled data often limits the application of supervised deep learning techniques for medical image segmentation. This has motivated the development of semi-supervised techniques that learn from a mixture of labeled and unlabeled images. In this paper, we propose a novel semi-supervised method that, in addition to supervised learning on labeled training images, learns to predict segmentations consistent under a given class of transformations on both labeled and unlabeled images. More specifically, in this work we explore learning equivariance to elastic deformations. We implement this through: 1) a Siamese architecture with two identical branches, each of which receives a differently transformed image, and 2) a composite loss function with a supervised segmentation loss term and an unsupervised term that encourages segmentation consistency between the predictions of the two branches. We evaluate the method on a public dataset of chest radiographs with segmentations of anatomical structures using 5-fold cross-validation. The proposed method reaches significantly higher segmentation accuracy compared to supervised learning. This is due to learning transformation consistency on both labeled and unlabeled images, with the latter contributing the most. We achieve the performance comparable to state-of-the-art chest X-ray segmentation methods while using substantially fewer labeled images.