# Semi-supervised few-shot learning for medical image segmentation

arXiv: https://arxiv.org/abs/2003.08462 · HTML: https://mlsanity.com/paper/2003.08462/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 30 of 34)

- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2207.08518/) (2022)
- [TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation](https://mlsanity.com/paper/2208.00713/) (2022)
- [Few-shot Segmentation with Optimal Transport Matching and Message Flow](https://mlsanity.com/paper/2108.08518/) (2021)
- [Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2308.07624/) (2023)
- [TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model](https://mlsanity.com/paper/2207.13415/) (2022)
- [Contextual Attention Network: Transformer Meets U-Net](https://mlsanity.com/paper/2203.01932/) (2022)
- [SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities](https://mlsanity.com/paper/2204.02961/) (2022)
- [DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2212.13504/) (2022)
- [Neuralizer: General Neuroimage Analysis without Re-Training](https://mlsanity.com/paper/2305.02644/) (2023)
- [Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation](https://mlsanity.com/paper/2210.16898/) (2022)
- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration](https://mlsanity.com/paper/2209.05160/) (2022)
- [Meta-learning with implicit gradients in a few-shot setting for medical image segmentation](https://mlsanity.com/paper/2106.03223/) (2021)
- [Challenges for machine learning in clinical translation of big data imaging studies](https://mlsanity.com/paper/2107.05630/) (2021)
- [ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models](https://mlsanity.com/paper/2407.07042/) (2024)
- [Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images](https://mlsanity.com/paper/2105.06238/) (2021)
- [Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice](https://mlsanity.com/paper/2102.01979/) (2021)
- [Active learning using adaptable task-based prioritisation](https://mlsanity.com/paper/2212.01703/) (2022)
- [Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach](https://mlsanity.com/paper/2307.14446/) (2023)
- [PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning](https://mlsanity.com/paper/2108.11694/) (2021)
- [Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning](https://mlsanity.com/paper/2201.05151/) (2022)
- [Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentation](https://mlsanity.com/paper/2204.01712/) (2022)
- [Segmentation by registration-enabled SAM prompt engineering using five reference images](https://mlsanity.com/paper/2407.17933/) (2024)
- [Weakly Supervised Volumetric Image Segmentation with Deformed Templates](https://mlsanity.com/paper/2106.03987/) (2021)
- [Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach](https://mlsanity.com/paper/2204.02943/) (2022)
- [Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning](https://mlsanity.com/paper/2103.02982/) (2021)
- [Joint Calibrationless Reconstruction and Segmentation of Parallel MRI](https://mlsanity.com/paper/2105.09220/) (2021)
- [Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited triplets](https://mlsanity.com/paper/2204.07824/) (2022)
- [Knowledge Consultation for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2503.10693/) (2025)

## Abstract (arXiv)

Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models require large amounts of pixel-level ground truth masks, which can be prohibitive to obtain in the medical domain. Furthermore, training such models in a low-data regime highly increases the risk of overfitting. Recent attempts to alleviate the need for large annotated datasets have developed training strategies under the few-shot learning paradigm, which addresses this shortcoming by learning a novel class from only a few labeled examples. In this context, a segmentation model is trained on episodes, which represent different segmentation problems, each of them trained with a very small labeled dataset. In this work, we propose a novel few-shot learning framework for semantic segmentation, where unlabeled images are also made available at each episode. To handle this new learning paradigm, we propose to include surrogate tasks that can leverage very powerful supervisory signals --derived from the data itself-- for semantic feature learning. We show that including unlabeled surrogate tasks in the episodic training leads to more powerful feature representations, which ultimately results in better generability to unseen tasks. We demonstrate the efficiency of our method in the task of skin lesion segmentation in two publicly available datasets. Furthermore, our approach is general and model-agnostic, which can be combined with different deep architectures.