# MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation

arXiv: https://arxiv.org/abs/2409.19483 · HTML: https://mlsanity.com/paper/2409.19483/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 69 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 30 of 44)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Extract Free Dense Labels from CLIP](https://mlsanity.com/paper/2112.01071/) (2021)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents](https://mlsanity.com/paper/2303.07240/) (2023)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [Decoupling Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2112.07910/) (2021)
- [Decoupled Contrastive Learning](https://mlsanity.com/paper/2110.06848/) (2021)
- [A General Framework for Uncertainty Estimation in Deep Learning](https://mlsanity.com/paper/1907.06890/) (2019)
- [CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2212.09506/) (2022)
- [FeatUp: A Model-Agnostic Framework for Features at Any Resolution](https://mlsanity.com/paper/2403.10516/) (2024)
- [Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language](https://mlsanity.com/paper/2212.07525/) (2022)
- [Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training](https://mlsanity.com/paper/2301.02280/) (2023)
- [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.13385/) (2023)
- [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023)
- [AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder](https://mlsanity.com/paper/2306.06370/) (2023)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [Multimodal Self-Supervised Learning for Medical Image Analysis](https://mlsanity.com/paper/1912.05396/) (2019)
- [Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2305.05803/) (2023)
- [How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images](https://mlsanity.com/paper/2306.13731/) (2023)
- [ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation](https://mlsanity.com/paper/2401.12665/) (2024)
- [ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image](https://mlsanity.com/paper/2312.07381/) (2023)
- [SAMAug: Point Prompt Augmentation for Segment Anything Model](https://mlsanity.com/paper/2307.01187/) (2023)

## Built on by (most cited first, 30 of 39)

- [BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models](https://mlsanity.com/paper/2411.15232/) (2024)
- [DCFormer: Efficient 3D Vision-Language Modeling with Decomposed Convolutions](https://mlsanity.com/paper/2502.05091/) (2025)
- [MedicoSAM: Robust Improvement of SAM for Medical Imaging](https://mlsanity.com/paper/2501.11734/) (2025)
- [Applications of Large Models in Medicine](https://mlsanity.com/paper/2502.17132/) (2025)
- [Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?](https://mlsanity.com/paper/2507.11569/) (2025)
- [TextSAM-EUS: Text Prompt Learning for SAM to Accurately Segment Pancreatic Tumor in Endoscopic Ultrasound](https://mlsanity.com/paper/2507.18082/) (2025)
- [Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging](https://mlsanity.com/paper/2503.02824/) (2025)
- [MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2602.20423/) (2026)
- [Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models](https://mlsanity.com/paper/2506.23903/) (2025)
- [CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values](https://mlsanity.com/paper/2509.03740/) (2025)
- [OmniRad: A Radiological Foundation Model for Multi-Task Medical Image Analysis](https://mlsanity.com/paper/2602.04547/) (2026)
- [Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection](https://mlsanity.com/paper/2411.07546/) (2024)
- [Zero-Shot Gaze-based Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2505.15256/) (2025)
- [Foundation Models in Biomedical Imaging: Turning Hype into Reality](https://mlsanity.com/paper/2512.15808/) (2025)
- [Text-guided multi-stage cross-perception network for medical image segmentation](https://mlsanity.com/paper/2506.07475/) (2025)
- [SAM-PTx: Text-Guided Fine-Tuning of SAM with Parameter-Efficient, Parallel-Text Adapters](https://mlsanity.com/paper/2508.00213/) (2025)
- [COME: Dual Structure-Semantic Learning with Collaborative MoE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets](https://mlsanity.com/paper/2508.09886/) (2025)
- [Breaking the Limits of Open-Weight CLIP: An Optimization Framework for Self-supervised Fine-tuning of CLIP](https://mlsanity.com/paper/2601.09859/) (2026)
- [If you can describe it, they can see it: Cross-Modal Learning of Visual Concepts from Textual Descriptions](https://mlsanity.com/paper/2411.15611/) (2024)
- [AutoMiSeg: Automatic Medical Image Segmentation via Test-Time Adaptation of Foundation Models](https://mlsanity.com/paper/2505.17931/) (2025)
- [Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research](https://mlsanity.com/paper/2506.13306/) (2025)
- [Prompt Mechanisms in Medical Imaging: A Comprehensive Survey](https://mlsanity.com/paper/2507.01055/) (2025)
- [Mask What Matters: Controllable Text-Guided Masking for Self-Supervised Medical Image Analysis](https://mlsanity.com/paper/2509.23054/) (2025)
- [SpinalSAM-R1: A Vision-Language Multimodal Interactive System for Spine CT Segmentation](https://mlsanity.com/paper/2511.00095/) (2025)
- [PPBoost: Progressive Prompt Boosting for Text-Driven Medical Image Segmentation](https://mlsanity.com/paper/2511.21984/) (2025)
- [ProGiDiff: Prompt-Guided Diffusion-Based Medical Image Segmentation](https://mlsanity.com/paper/2601.16060/) (2026)
- [XAI-CLIP: ROI-Guided Perturbation Framework for Explainable Medical Image Segmentation in Multimodal Vision-Language Models](https://mlsanity.com/paper/2602.07017/) (2026)
- [A Semi-Supervised Framework for Breast Ultrasound Segmentation with Training-Free Pseudo-Label Generation and Label Refinement](https://mlsanity.com/paper/2603.06167/) (2026)
- [CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation](https://mlsanity.com/paper/2605.24807/) (2026)
- [Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuning](https://mlsanity.com/paper/2605.26292/) (2026)

## Abstract (arXiv)

Segmentation of anatomical structures and pathological regions in medical images is essential for modern clinical diagnosis, disease research, and treatment planning. While significant advancements have been made in deep learning-based segmentation techniques, many of these methods still suffer from limitations in data efficiency, generalizability, and interactivity. As a result, developing precise segmentation methods that require fewer labeled datasets remains a critical challenge in medical image analysis. Recently, the introduction of foundation models like CLIP and Segment-Anything-Model (SAM), with robust cross-domain representations, has paved the way for interactive and universal image segmentation. However, further exploration of these models for data-efficient segmentation in medical imaging is still needed and highly relevant. In this paper, we introduce MedCLIP-SAMv2, a novel framework that integrates the CLIP and SAM models to perform segmentation on clinical scans using text prompts, in both zero-shot and weakly supervised settings. Our approach includes fine-tuning the BiomedCLIP model with a new Decoupled Hard Negative Noise Contrastive Estimation (DHN-NCE) loss, and leveraging the Multi-modal Information Bottleneck (M2IB) to create visual prompts for generating segmentation masks from SAM in the zero-shot setting. We also investigate using zero-shot segmentation labels within a weakly supervised paradigm to enhance segmentation quality further. Extensive testing across four diverse segmentation tasks and medical imaging modalities (breast tumor ultrasound, brain tumor MRI, lung X-ray, and lung CT) demonstrates the high accuracy of our proposed framework. Our code is available at https://github.com/HealthX-Lab/MedCLIP-SAMv2.