# SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation arXiv: https://arxiv.org/abs/2308.13759 ยท HTML: https://mlsanity.com/paper/2308.13759/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 19 of its 32 references that are in the map. - First posted on arXiv 26 Aug 2023. ## Builds on (most cited first, 19 of 19) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021) - [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019) - [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017) - [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) - [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020) - [In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning](https://mlsanity.com/paper/2101.06329/) (2021) - [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) - [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) - [Can SAM Segment Polyps?](https://mlsanity.com/paper/2304.07583/) (2023) - [Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2305.05803/) (2023) - [A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering](https://mlsanity.com/paper/2306.06211/) (2023) - [Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation](https://mlsanity.com/paper/2112.00735/) (2021) - [Robustness of SAM: Segment Anything Under Corruptions and Beyond](https://mlsanity.com/paper/2306.07713/) (2023) - [Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentation](https://mlsanity.com/paper/2110.08589/) (2021) - [Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2108.11154/) (2021) ## Built on by (most cited first, 12 of 12) - [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024) - [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024) - [Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2407.05416/) (2024) - [Recent Advances in Medical Imaging Segmentation: A Survey](https://mlsanity.com/paper/2505.09274/) (2025) - [Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.16997/) (2025) - [Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models](https://mlsanity.com/paper/2310.11173/) (2023) - [Topo-VM-UNetV2: Encoding Topology into Vision Mamba UNet for Polyp Segmentation](https://mlsanity.com/paper/2505.06210/) (2025) - [Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation](https://mlsanity.com/paper/2312.00312/) (2023) - [Adapting a Segmentation Foundation Model for Medical Image Classification](https://mlsanity.com/paper/2505.06217/) (2025) - [SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model](https://mlsanity.com/paper/2312.09899/) (2023) - [SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation](https://mlsanity.com/paper/2411.12602/) (2024) - [SSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2](https://mlsanity.com/paper/2512.11548/) (2025) ## Abstract (arXiv) The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images, serving as a versatile perceptual tool for various downstream image segmentation tasks. In contrast, medical image segmentation tasks often rely on domain-specific knowledge (DSK). In this paper, we propose a novel method that combines the segmentation foundation model (i.e., SAM) with domain-specific knowledge for reliable utilization of unlabeled images in building a medical image segmentation model. Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge. These two stages are repeated until no more samples are added to the labeled set. A novel optimal-matching-based method is developed for combining the SAM-generated segmentation proposals and pixel-level and image-level DSK for constructing annotations of unlabeled images in the iterative stage (2). In experiments, we demonstrate the effectiveness of our proposed method for breast cancer segmentation in ultrasound images, polyp segmentation in endoscopic images, and skin lesion segmentation in dermoscopic images. Our work initiates a new direction of semi-supervised learning for medical image segmentation: the segmentation foundation model can be harnessed as a valuable tool for label-efficient segmentation learning in medical image segmentation.