SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation

• URL: http://arxiv.org/abs/2602.19213v1
• Date: Sun, 22 Feb 2026 14:48:42 GMT
• Title: SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation
• Authors: Yujie Lu, Jingwen Li, Sibo Ju, Yanzhou Su, he yao, Yisong Liu, Min Zhu, Junlong Cheng,
• Abstract summary: We propose SegMoTE, an efficient and adaptive framework for medical image segmentation.<n>SegMoTE preserves SAM's original prompt interface, efficient inference, and zero-shot generalization.<n>SegMoTE achieves SOTA performance across diverse imaging modalities and anatomical tasks.
• Score: 18.723160085156717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation is vital for clinical diagnosis and quantitative analysis, yet remains challenging due to the heterogeneity of imaging modalities and the high cost of pixel-level annotations. Although general interactive segmentation models like SAM have achieved remarkable progress, their transfer to medical imaging still faces two key bottlenecks: (i) the lack of adaptive mechanisms for modality- and anatomy-specific tasks, which limits generalization in out-of-distribution medical scenarios; and (ii) current medical adaptation methods fine-tune on large, heterogeneous datasets without selection, leading to noisy supervision, higher cost, and negative transfer. To address these issues, we propose SegMoTE, an efficient and adaptive framework for medical image segmentation. SegMoTE preserves SAM's original prompt interface, efficient inference, and zero-shot generalization while introducing only a small number of learnable parameters to dynamically adapt across modalities and tasks. In addition, we design a progressive prompt tokenization mechanism that enables fully automatic segmentation, significantly reducing annotation dependence. Trained on MedSeg-HQ, a curated dataset less than 1% of existing large-scale datasets, SegMoTE achieves SOTA performance across diverse imaging modalities and anatomical tasks. It represents the first efficient, robust, and scalable adaptation of general segmentation models to the medical domain under extremely low annotation cost, advancing the practical deployment of foundation vision models in clinical applications.

Related papers

• Multimodal Causal-Driven Representation Learning for Generalizable Medical Image Segmentation [56.52520416420957]
  We propose Multimodal Causal-Driven Representation Learning (MCDRL) to tackle domain generalization in medical image segmentation.<n>MCDRL consistently outperforms competing methods, yielding superior segmentation accuracy and exhibiting robust generalizability.
  arXiv  Detail & Related papers  (2025-08-07T03:41:41Z)
• Dynamically evolving segment anything model with continuous learning for medical image segmentation [50.92344083895528]
  We introduce EvoSAM, a dynamically evolving medical image segmentation model.<n>EvoSAM continuously accumulates new knowledge from an ever-expanding array of scenarios and tasks.<n>Experiments conducted by surgical clinicians on blood vessel segmentation confirm that EvoSAM enhances segmentation efficiency based on user prompts.
  arXiv  Detail & Related papers  (2025-03-08T14:37:52Z)
• Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation [30.524999223901645]
  We propose an enhanced Segment Anything Model (SAM) framework that utilizes annotation-efficient prompts generated in a fully unsupervised fashion.<n>We adopt the direct preference optimization technique to design an optimal policy that enables the model to generate high-fidelity segmentations.<n>State-of-the-art performance of our framework in tasks such as lung segmentation, breast tumor segmentation, and organ segmentation across various modalities, including X-ray, ultrasound, and abdominal CT, justifies its effectiveness in low-annotation data scenarios.
  arXiv  Detail & Related papers  (2025-03-06T17:28:48Z)
• MGFI-Net: A Multi-Grained Feature Integration Network for Enhanced Medical Image Segmentation [0.3108011671896571]
  A major challenge in medical image segmentation is achieving accurate delineation of regions of interest in the presence of noise, low contrast, or complex anatomical structures.<n>Existing segmentation models often neglect the integration of multi-grained information and fail to preserve edge details.<n>We propose a novel image semantic segmentation model called the Multi-Grained Feature Integration Network (MGFI-Net)<n>Our MGFI-Net is designed with two dedicated modules to tackle these issues.
  arXiv  Detail & Related papers  (2025-02-19T15:24:34Z)
• Efficient MedSAMs: Segment Anything in Medical Images on Laptop [69.28565867103542]
  We organized the first international competition dedicated to promptable medical image segmentation.<n>The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline.<n>The best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption.
  arXiv  Detail & Related papers  (2024-12-20T17:33:35Z)
• MRGen: Segmentation Data Engine for Underrepresented MRI Modalities [59.61465292965639]
  Training medical image segmentation models for rare yet clinically important imaging modalities is challenging due to the scarcity of annotated data.<n>This paper investigates leveraging generative models to synthesize data, for training segmentation models for underrepresented modalities.<n>We present MRGen, a data engine for controllable medical image synthesis conditioned on text prompts and segmentation masks.
  arXiv  Detail & Related papers  (2024-12-04T16:34:22Z)
• MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation [2.2585213273821716]
  We introduce MedCLIP-SAMv2, a novel framework that integrates the CLIP and SAM models to perform segmentation on clinical scans.<n>Our approach includes fine-tuning the BiomedCLIP model with a new Decoupled Hard Negative Noise Contrastive Estimation (DHN-NCE) loss.<n>We also investigate using zero-shot segmentation labels within a weakly supervised paradigm to enhance segmentation quality further.
  arXiv  Detail & Related papers  (2024-09-28T23:10:37Z)
• Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models [17.461510586128874]
  We propose a novel method that adapts DINOv2 and Segment Anything Model 2 for retrieval-augmented few-shot medical image segmentation.<n>Our approach uses DINOv2's feature as query to retrieve similar samples from limited annotated data, which are then encoded as memories and stored in memory bank.
  arXiv  Detail & Related papers  (2024-08-16T15:48:07Z)
• MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation [2.2585213273821716]
  We propose a novel framework, called MedCLIP-SAM, that combines CLIP and SAM models to generate segmentation of clinical scans. By extensively testing three diverse segmentation tasks and medical image modalities, our proposed framework has demonstrated excellent accuracy.
  arXiv  Detail & Related papers  (2024-03-29T15:59:11Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.