# Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

arXiv: https://arxiv.org/abs/2103.00429 · HTML: https://mlsanity.com/paper/2103.00429/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 205 of its 331 references that are in the map.
- First posted on arXiv 28 Feb 2021.

## Builds on (most cited first, 30 of 205)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)

## Built on by (most cited first, 22 of 22)

- [Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models](https://mlsanity.com/paper/2408.08813/) (2024)
- [Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2304.07519/) (2023)
- [Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge](https://mlsanity.com/paper/2110.01607/) (2021)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Did You Get What You Paid For? Rethinking Annotation Cost of Deep Learning Based Computer Aided Detection in Chest Radiographs](https://mlsanity.com/paper/2209.15314/) (2022)
- [WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy](https://mlsanity.com/paper/2210.13109/) (2022)
- [Weakly Semi-Supervised Detection in Lung Ultrasound Videos](https://mlsanity.com/paper/2308.04463/) (2023)
- [Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation](https://mlsanity.com/paper/2404.00667/) (2024)
- [Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.06912/) (2023)
- [Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation](https://mlsanity.com/paper/2202.02371/) (2022)
- [Sparse annotation strategies for segmentation of short axis cardiac MRI](https://mlsanity.com/paper/2307.12619/) (2023)
- [Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation](https://mlsanity.com/paper/2401.08422/) (2024)
- [Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI](https://mlsanity.com/paper/2311.07234/) (2023)
- [Leveraging Weak Supervision for Cell Localization in Digital Pathology Using Multitask Learning and Consistency Loss](https://mlsanity.com/paper/2412.15392/) (2024)
- [Improving World Models using Deep Supervision with Linear Probes](https://mlsanity.com/paper/2504.03861/) (2025)
- [TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histology](https://mlsanity.com/paper/2504.12718/) (2025)
- [Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentation](https://mlsanity.com/paper/2505.19208/) (2025)
- [Out-of-distribution data supervision towards biomedical semantic segmentation](https://mlsanity.com/paper/2507.12105/) (2025)
- [Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling](https://mlsanity.com/paper/2509.13084/) (2025)
- [Leveraging Unlabeled Scans for NCCT Image Segmentation in Early Stroke Diagnosis: A Semi-Supervised GAN Approach](https://mlsanity.com/paper/2511.19576/) (2025)
- [Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM](https://mlsanity.com/paper/2604.23314/) (2026)
- [Active few-shot segmentation by reinforcing data selection](https://mlsanity.com/paper/2607.22371/) (2026)

## Abstract (arXiv)

Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks. The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling. Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful application of deep learning models in medical image segmentation. However, due to its intrinsic difficulty, segmentation with limited supervision is challenging and specific model design and/or learning strategies are needed. In this paper, we provide a systematic and up-to-date review of the solutions above, with summaries and comments about the methodologies. We also highlight several problems in this field, discussed future directions observing further investigations.