# Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation

arXiv: https://arxiv.org/abs/2407.05416 · HTML: https://mlsanity.com/paper/2407.05416/

## Key facts

- Cited by 16 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 7 Jul 2024.

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

- [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)
- [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)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.09910/) (2022)
- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)
- [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)
- [Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning](https://mlsanity.com/paper/1909.01804/) (2019)
- [MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation](https://mlsanity.com/paper/2309.08842/) (2023)
- [SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization](https://mlsanity.com/paper/2312.06316/) (2023)
- [SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation](https://mlsanity.com/paper/2308.13759/) (2023)
- [ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation](https://mlsanity.com/paper/2312.07969/) (2023)

## Built on by (most cited first, 16 of 16)

- [Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2411.12814/) (2024)
- [Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2412.13742/) (2024)
- [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)
- [S^4M: Boosting Semi-Supervised Instance Segmentation with SAM](https://mlsanity.com/paper/2504.05301/) (2025)
- [ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction](https://mlsanity.com/paper/2507.15803/) (2025)
- [Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges](https://mlsanity.com/paper/2507.09562/) (2025)
- [Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy](https://mlsanity.com/paper/2410.07460/) (2024)
- [Iterative pseudo-labeling based adaptive copy-paste supervision for semi-supervised tumor segmentation](https://mlsanity.com/paper/2508.04044/) (2025)
- [From Specialist to Generalist: Unlocking SAM's Learning Potential on Unlabeled Medical Images](https://mlsanity.com/paper/2601.17934/) (2026)
- [SemSim: Revisiting Weak-to-Strong Consistency from a Semantic Similarity Perspective for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2410.13486/) (2024)
- [Rapid Bone Scintigraphy Enhancement via Semantic Prior Distillation from Segment Anything Model](https://mlsanity.com/paper/2503.02321/) (2025)
- [Uncertainty-Aware Extreme Point Tracing for Weakly Supervised Ultrasound Image Segmentation](https://mlsanity.com/paper/2510.15666/) (2025)
- [SSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2](https://mlsanity.com/paper/2512.11548/) (2025)
- [Harmonizing Generalization and Specialization: Uncertainty-Informed Collaborative Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2512.13101/) (2025)
- [Uncertainty-Aware Concept and Motion Segmentation for Semi-Supervised Angiography Videos](https://mlsanity.com/paper/2603.00881/) (2026)
- [Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM](https://mlsanity.com/paper/2604.23314/) (2026)

## Abstract (arXiv)

Semi-supervised learning (SSL) has achieved notable progress in medical image segmentation. To achieve effective SSL, a model needs to be able to efficiently learn from limited labeled data and effectively exploiting knowledge from abundant unlabeled data. Recent developments in visual foundation models, such as the Segment Anything Model (SAM), have demonstrated remarkable adaptability with improved sample efficiency. To harness the power of foundation models for application in SSL, we propose a cross prompting consistency method with segment anything model (CPC-SAM) for semi-supervised medical image segmentation. Our method employs SAM's unique prompt design and innovates a cross-prompting strategy within a dual-branch framework to automatically generate prompts and supervisions across two decoder branches, enabling effectively learning from both scarce labeled and valuable unlabeled data. We further design a novel prompt consistency regularization, to reduce the prompt position sensitivity and to enhance the output invariance under different prompts. We validate our method on two medical image segmentation tasks. The extensive experiments with different labeled-data ratios and modalities demonstrate the superiority of our proposed method over the state-of-the-art SSL methods, with more than 9% Dice improvement on the breast cancer segmentation task.