# CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation arXiv: https://arxiv.org/abs/2411.10136 ยท HTML: https://mlsanity.com/paper/2411.10136/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 47 references that are in the map. - First posted on arXiv 15 Nov 2024. ## Builds on (most cited first, 30 of 32) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [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) - [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021) - [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023) - [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019) - [Domain Adaptive Ensemble Learning](https://mlsanity.com/paper/2003.07325/) (2020) - [Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains](https://mlsanity.com/paper/2007.02035/) (2020) - [Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning](https://mlsanity.com/paper/2004.01959/) (2020) - [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017) - [Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment](https://mlsanity.com/paper/2303.13662/) (2023) - [Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding](https://mlsanity.com/paper/2403.18271/) (2024) - [Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2211.14805/) (2022) - [Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation](https://mlsanity.com/paper/2106.13292/) (2021) - [Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2109.05676/) (2021) - [DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2306.00499/) (2023) - [APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation](https://mlsanity.com/paper/2406.08372/) (2024) - [UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images](https://mlsanity.com/paper/2402.16663/) (2024) - [Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2307.09005/) (2023) - [Localized Adversarial Domain Generalization](https://mlsanity.com/paper/2205.04114/) (2022) - [MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization](https://mlsanity.com/paper/2303.12649/) (2023) - [Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation](https://mlsanity.com/paper/2305.19949/) (2023) - [ASPS: Augmented Segment Anything Model for Polyp Segmentation](https://mlsanity.com/paper/2407.00718/) (2024) - [De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation](https://mlsanity.com/paper/2407.14153/) (2024) - [S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2408.06447/) (2024) - [A Survey on Domain Generalization for Medical Image Analysis](https://mlsanity.com/paper/2402.05035/) (2024) - [Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2409.12522/) (2024) - [Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation](https://mlsanity.com/paper/2304.02720/) (2023) ## Built on by (most cited first, 5 of 5) - [OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation](https://mlsanity.com/paper/2503.07098/) (2025) - [MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic Segmentation](https://mlsanity.com/paper/2503.06700/) (2025) - [Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study](https://mlsanity.com/paper/2503.22862/) (2025) - [Optimization of MedSAM model based on bounding box adaptive perturbation algorithm](https://mlsanity.com/paper/2503.19700/) (2025) - [EviATTA: Evidential Active Test-Time Adaptation for Medical Segment Anything Models](https://mlsanity.com/paper/2603.14666/) (2026) ## Abstract (arXiv) Medical images often exhibit distribution shifts due to variations in imaging protocols and scanners across different medical centers. Domain Generalization (DG) methods aim to train models on source domains that can generalize to unseen target domains. Recently, the segment anything model (SAM) has demonstrated strong generalization capabilities due to its prompt-based design, and has gained significant attention in image segmentation tasks. Existing SAM-based approaches attempt to address the need for manual prompts by introducing prompt generators that automatically generate these prompts. However, we argue that auto-generated prompts may not be sufficiently accurate under distribution shifts, potentially leading to incorrect predictions that still require manual verification and correction by clinicians. To address this challenge, we propose a method for 2D medical image segmentation called Self-Correcting SAM (CoSAM). Our approach begins by generating coarse masks using SAM in a prompt-free manner, providing prior prompts for the subsequent stages, and eliminating the need for prompt generators. To automatically refine these coarse masks, we introduce a generalized error decoder that simulates the correction process typically performed by clinicians. Furthermore, we generate diverse prompts as feedback based on the corrected masks, which are used to iteratively refine the predictions within a self-correcting loop, enhancing the generalization performance of our model. Extensive experiments on two medical image segmentation benchmarks across multiple scenarios demonstrate the superiority of CoSAM over state-of-the-art SAM-based methods.