# Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation

arXiv: https://arxiv.org/abs/2412.13742 · HTML: https://mlsanity.com/paper/2412.13742/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation.
- Builds on 34 of its 68 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Placed in

- Knowledge distillation / [Distillation for medical imaging](https://mlsanity.com/distill/distillation-for-medical-imaging/)

## Builds on (most cited first, 30 of 34)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [Semi-supervised Medical Image Segmentation through Dual-task Consistency](https://mlsanity.com/paper/2009.04448/) (2020)
- [Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.09910/) (2022)
- [Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.00673/) (2023)
- [Mutual Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2109.09960/) (2021)
- [Semi-supervised Left Atrium Segmentation with Mutual Consistency Training](https://mlsanity.com/paper/2103.02911/) (2021)
- [SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.06227/) (2021)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2304.09148/) (2023)
- [Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation](https://mlsanity.com/paper/2109.07407/) (2021)
- [Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation](https://mlsanity.com/paper/2201.08657/) (2022)
- [Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2307.06312/) (2023)
- [Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation](https://mlsanity.com/paper/2206.02307/) (2022)
- [Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2301.04465/) (2023)
- [SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization](https://mlsanity.com/paper/2312.06316/) (2023)
- [Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.05416/) (2024)
- [S2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation](https://mlsanity.com/paper/2306.00451/) (2023)
- [Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.08951/) (2024)

## Built on by (most cited first, 23 of 23)

- [Vision Foundation Models in Medical Image Analysis: Advances and Challenges](https://mlsanity.com/paper/2502.14584/) (2025)
- [MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2508.03511/) (2025)
- [Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2508.09014/) (2025)
- [Text-driven Multiplanar Visual Interaction for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2507.12382/) (2025)
- [UCS: A Universal Model for Curvilinear Structure Segmentation](https://mlsanity.com/paper/2504.04034/) (2025)
- [Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches](https://mlsanity.com/paper/2506.10825/) (2025)
- [From Specialist to Generalist: Unlocking SAM's Learning Potential on Unlabeled Medical Images](https://mlsanity.com/paper/2601.17934/) (2026)
- [ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentation](https://mlsanity.com/paper/2606.20161/) (2026)
- [Prompt Mechanisms in Medical Imaging: A Comprehensive Survey](https://mlsanity.com/paper/2507.01055/) (2025)
- [TOM: An Open-Source Tongue Segmentation Method with Multi-Teacher Distillation and Task-Specific Data Augmentation](https://mlsanity.com/paper/2508.14932/) (2025)
- [Dual-Scale Volume Priors with Wasserstein-Based Consistency for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2509.04273/) (2025)
- [Vision-Language Enhanced Foundation Model for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2511.19759/) (2025)
- [XAI-CLIP: ROI-Guided Perturbation Framework for Explainable Medical Image Segmentation in Multimodal Vision-Language Models](https://mlsanity.com/paper/2602.07017/) (2026)
- [Uncertainty-Aware Concept and Motion Segmentation for Semi-Supervised Angiography Videos](https://mlsanity.com/paper/2603.00881/) (2026)
- [Prompt-Free Lightweight SAM Adaptation for Histopathology Nuclei Segmentation with Strong Cross-Dataset Generalization](https://mlsanity.com/paper/2603.20326/) (2026)
- [RAP: Retrieve, Adapt, and Prompt-Fit for Training-Free Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2603.27705/) (2026)
- [SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation](https://mlsanity.com/paper/2604.22825/) (2026)
- [SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2604.23274/) (2026)
- [Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation](https://mlsanity.com/paper/2605.08238/) (2026)
- [OBBSeg: Irregular Lesion Segmentation under Oriented Bounding Box Annotations](https://mlsanity.com/paper/2607.06007/) (2026)
- [OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background](https://mlsanity.com/paper/2607.16705/) (2026)
- [ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Image](https://mlsanity.com/paper/2607.24453/) (2026)
- [Prompt and Refinement: Asymmetric Mutual Learning for Infrared Small Target Detection with Noisy Labels](https://mlsanity.com/paper/2610.05918/) (2026)

## Abstract (arXiv)

The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust generalization capabilities. However, applying these models directly to medical image segmentation still exposes performance degradation. In this paper, we propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation. Firstly, we propose a Multi-view Co-training (MC) strategy that employs two distinct sub-networks to employ a co-teaching paradigm, resulting in more robust outcomes. Secondly, we present a Learnable Prompt Strategy (LPS) to dynamically produce dense prompts and integrate an adapter to fine-tune SAM specifically for medical image segmentation tasks. Moreover, we propose SAM-induced Knowledge Distillation (SKD) to transfer useful knowledge from SAM to two sub-networks, enabling them to learn from SAM's predictions and alleviate the effects of incorrect pseudo-labels during training. Notably, the predictions generated by our subnets are used to produce mask prompts for SAM, facilitating effective inter-module information exchange. Extensive experimental results on various medical segmentation tasks demonstrate that our model outperforms the state-of-the-art semi-supervised segmentation approaches. Crucially, our SAM distillation framework can be seamlessly integrated into other semi-supervised segmentation methods to enhance performance. The code will be released upon acceptance of this manuscript at: https://github.com/taozh2017/KnowSAM