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Customized Segment Anything Model for Medical Image Segmentation
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We propose SAMed, a general solution for medical image segmentation. Different from the previous methods, SAMed is built upon the large-scale image segmentation model, Segment Anything Model (SAM), to explore the new research paradigm of customizing large-scale models for medical image segmentation. SAMed applies the low-rank-based (LoRA) finetuning strategy to the SAM image encoder and finetunes it together with the prompt encoder and the mask decoder on labeled medical image segmentation datasets. We also observe the warmup finetuning strategy and the AdamW optimizer lead SAMed to successful convergence and lower loss. Different from SAM, SAMed could perform semantic segmentation on medical images. Our trained SAMed model achieves 81.88 DSC and 20.64 HD on the Synapse multi-organ segmentation dataset, which is on par with the state-of-the-art methods. We conduct extensive experiments to validate the effectiveness of our design. Since SAMed only updates a small fraction of the SAM parameters, its deployment cost and storage cost are quite marginal in practical usage. The code of SAMed is available at https://github.com/hitachinsk/SAMed.
Forward citations
Cited by 18 Pith papers
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Weakly Supervised Segmentation as Semantic-Based Regularization
Differentiable fuzzy logic constraints fine-tune SAM to generate higher-quality pseudo-labels, enabling a second-stage model to reach state-of-the-art weakly supervised segmentation on Pascal VOC and REFUGE2, sometime...
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HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation
HPR-SAM replaces manual prompts in SAM with hierarchical probabilistic anatomical representations, achieving state-of-the-art medical image segmentation on Synapse, LA, and PROMISE12 datasets.
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Weakly Supervised Segmentation as Semantic-Based Regularization
A neurosymbolic approach uses fuzzy logic constraints to refine SAM under weak supervision, producing improved pseudo-labels that enable state-of-the-art segmentation on Pascal VOC and REFUGE2.
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DuetFair: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Segmentation
DuetFair couples inter-subgroup adaptation with intra-subgroup robustness via FairDRO (dMoE plus subgroup-conditioned DRO) to boost worst-case and equity-scaled performance on medical segmentation benchmarks.
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SemiSAM-O1: How far can we push the boundary of annotation-efficient medical image segmentation?
SemiSAM-O1 narrows the gap to fully supervised medical image segmentation performance while using only a single annotated template image through foundation-model feature propagation and uncertainty-guided iterative re...
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RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
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Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain p...
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An Edge-aware Prompt-enhanced SAM for Ultrasound Image Segmentation
EP-SAM improves ultrasound image segmentation by injecting edge-aware features and self-generated mask prompts into SAM's encoder pipeline.
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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
APRIL-MedSeg is a new open-source modular toolbox that uses YAML configuration and component registries to unify multiple advanced paradigms for medical image segmentation.
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Attenuation-Resilient Alternating Optimization for Laparoscopic Liver Landmark Detection
A2ONet improves robustness of liver surface landmark detection in laparoscopic surgery via illumination field compensation, frequency-orientation selective filtering, and alternating seg-curve optimization, with repor...
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M$^4$-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection
M⁴-SAM equips SAM2 with modality-aware MoE-LoRA, gated multi-level fusion, and pseudo-guided initialization to reach state-of-the-art on RGB-D video salient object detection.
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Frequency Adapter with SAM for Generalized Medical Image Segmentation
FSAM integrates a frequency adapter into SAM with LoRA to extract domain-invariant high-frequency features and outperforms prior domain generalization methods on fundus and prostate datasets.
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Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation
RL4Seg3D applies reinforcement learning with novel reward functions and fusion to adapt echocardiography segmentation models across domains, improving accuracy, anatomical validity, and temporal consistency on over 30...
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APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
Presents APRIL-MedSeg, a modular YAML-configurable toolbox for 2D medical image segmentation integrating semi-supervised, domain adaptation, distillation, weakly supervised, text-guided, and foundation model paradigms...
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Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images
LoRA-adapted SAM 3 with hard-negative mining and phase-coherent filtering achieves median Dice 0.968 on pulmonary structures from 4DCT using seven annotated volumes.
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Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
Fine-tuned foundation models produce reliable MSK MRI biomarkers that support workload-reducing triage and calibrated 48-month prediction of knee replacement and incident OA.
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Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes
GSAM applies random cropping to enable variable input sizes for efficient SAM fine-tuning, claiming lower compute with comparable or higher accuracy on varied datasets.
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Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation
Dante is a new open-source backend for the Dafne ecosystem that implements configurable training from scratch, layer freezing, and channel-wise LoRA for medical image segmentation, with validation showing faster conve...
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