# Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation

arXiv: https://arxiv.org/abs/2308.07624 · HTML: https://mlsanity.com/paper/2308.07624/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)
- [Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications](https://mlsanity.com/paper/2304.05750/) (2023)
- [Can SAM Segment Polyps?](https://mlsanity.com/paper/2304.07583/) (2023)
- [When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation](https://mlsanity.com/paper/2304.08506/) (2023)
- [Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding](https://mlsanity.com/paper/2012.05440/) (2020)
- [Semi-supervised few-shot learning for medical image segmentation](https://mlsanity.com/paper/2003.08462/) (2020)
- [FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition](https://mlsanity.com/paper/2305.17421/) (2023)
- [MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation](https://mlsanity.com/paper/2109.09734/) (2021)

## Built on by (most cited first, 30 of 35)

- [MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation](https://mlsanity.com/paper/2309.08842/) (2023)
- [Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery](https://mlsanity.com/paper/2401.06013/) (2024)
- [EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera](https://mlsanity.com/paper/2405.08672/) (2024)
- [MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation](https://mlsanity.com/paper/2409.19483/) (2024)
- [How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model](https://mlsanity.com/paper/2404.09957/) (2024)
- [CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model](https://mlsanity.com/paper/2402.03631/) (2024)
- [Biomedical SAM 2: Segment Anything in Biomedical Images and Videos](https://mlsanity.com/paper/2408.03286/) (2024)
- [De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation](https://mlsanity.com/paper/2407.14153/) (2024)
- [SAM-based instance segmentation models for the automation of structural damage detection](https://mlsanity.com/paper/2401.15266/) (2024)
- [MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical Maps](https://mlsanity.com/paper/2411.06971/) (2024)
- [Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts](https://mlsanity.com/paper/2409.06821/) (2024)
- [Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision](https://mlsanity.com/paper/2409.20293/) (2024)
- [Systematic Evaluation and Guidelines for Segment Anything Model in Surgical Video Analysis](https://mlsanity.com/paper/2501.00525/) (2024)
- [TextSAM-EUS: Text Prompt Learning for SAM to Accurately Segment Pancreatic Tumor in Endoscopic Ultrasound](https://mlsanity.com/paper/2507.18082/) (2025)
- [PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2501.06692/) (2025)
- [Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation](https://mlsanity.com/paper/2404.17033/) (2024)
- [CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching](https://mlsanity.com/paper/2407.06795/) (2024)
- [Prompt-Tuning SAM: From Generalist to Specialist with only 2048 Parameters and 16 Training Images](https://mlsanity.com/paper/2504.16739/) (2025)
- [Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges](https://mlsanity.com/paper/2507.09562/) (2025)
- [SqueezeSAM: User friendly mobile interactive segmentation](https://mlsanity.com/paper/2312.06736/) (2023)
- [CodeSCM: Causal Analysis for Multi-Modal Code Generation](https://mlsanity.com/paper/2502.05150/) (2025)
- [SurgiSAM2: Fine-tuning a foundational model for surgical video anatomy segmentation and detection](https://mlsanity.com/paper/2503.03942/) (2025)
- [Prompt learning with bounding box constraints for medical image segmentation](https://mlsanity.com/paper/2507.02743/) (2025)
- [Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting](https://mlsanity.com/paper/2511.19953/) (2025)
- [Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation](https://mlsanity.com/paper/2502.03501/) (2025)
- [MapSAM2: Adapting SAM2 for Automatic Segmentation of Historical Map Images and Time Series](https://mlsanity.com/paper/2510.27547/) (2025)
- [MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection](https://mlsanity.com/paper/2511.04255/) (2025)
- [Focus on Background: Exploring SAM's Potential in Few-shot Medical Image Segmentation with Background-centric Prompting](https://mlsanity.com/paper/2603.21287/) (2026)
- [Mask to Concept: Auto-Promptable SAM3 via Efficient Test-Time Concept Embedding Search for Few-Shot Annotation](https://mlsanity.com/paper/2606.26711/) (2026)
- [Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation](https://mlsanity.com/paper/2410.00979/) (2024)

## Abstract (arXiv)

Recent advancements in large foundation models have shown promising potential in the medical industry due to their flexible prompting capability. One such model, the Segment Anything Model (SAM), a prompt-driven segmentation model, has shown remarkable performance improvements, surpassing state-of-the-art approaches in medical image segmentation. However, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. In this paper, we propose a novel perspective on self-prompting in medical vision applications. Specifically, we harness the embedding space of SAM to prompt itself through a simple yet effective linear pixel-wise classifier. By preserving the encoding capabilities of the large model, the contextual information from its decoder, and leveraging its interactive promptability, we achieve competitive results on multiple datasets (i.e. improvement of more than 15% compared to fine-tuning the mask decoder using a few images).