TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation

• URL: http://arxiv.org/abs/2604.10912v1
• Date: Mon, 13 Apr 2026 02:31:22 GMT
• Title: TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation
• Abstract summary: We propose a text-guided segmentation framework that incorporates clinical language prompts and semantic distillation as auxiliary semantic cues.<n>TAMISeg integrates a consistency-aware encoder pretrained with strong perturbations for robust feature extraction.<n>Experiments on the Kvasir-SEG, MosMedData+, and QaTa-COV19 datasets demonstrate that TAMISeg consistently outperforms existing uni-modal and multi-modal methods in both qualitative and quantitative evaluations.
• Score: 16.600598318231288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image segmentation remains challenging due to limited fine-grained annotations, complex anatomical structures, and image degradation from noise, low contrast, or illumination variation. We propose TAMISeg, a text-guided segmentation framework that incorporates clinical language prompts and semantic distillation as auxiliary semantic cues to enhance visual understanding and reduce reliance on pixel-level fine-grained annotations. TAMISeg integrates three core components: a consistency-aware encoder pretrained with strong perturbations for robust feature extraction, a semantic encoder distillation module with supervision from a frozen DINOv3 teacher to enhance semantic discriminability, and a scale-adaptive decoder that segments anatomical structures across different spatial scales. Experiments on the Kvasir-SEG, MosMedData+, and QaTa-COV19 datasets demonstrate that TAMISeg consistently outperforms existing uni-modal and multi-modal methods in both qualitative and quantitative evaluations. Code will be made publicly available at https://github.com/qczggaoqiang/TAMISeg.

Related papers

• Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation [3.182068203318624]
  We propose Med DisSeg, a dispersion driven medical image segmentation framework.<n>Med DisSeg combines a lightweight Dispersive Loss with adaptive attention for fine grained structure segmentation.<n>We show that Med DisSeg achieves competitive results on multi organ CT segmentation, supporting its robustness and cross task applicability.
  arXiv  Detail & Related papers  (2026-05-14T08:49:36Z)
• Multi-View Synergistic Learning with Vision-Language Adaption for Low-Resource Biomedical Image Classification [59.24009931000134]
  Multi-View Synergistic Learning (MVSL) is a unified framework that addresses adaptation paradigms, representation granularity, and disease semantic relationships.<n>MVSL decouples the adaptation of visual and textual encoders to respect their distinct representational characteristics.<n>It further introduces multi-granularity contrastive learning to explicitly model both global image semantics and localized lesion-level evidence.<n>MVSL consistently outperforms state-of-the-art methods in few-shot and zero-shot classification settings.
  arXiv  Detail & Related papers  (2026-04-27T02:41:27Z)
• Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation [51.509572354327986]
  This work introduces a novel two-stage (structure- and report-learning) framework tailored for Computed Tomography Report Generation (CTRG)<n>In the first stage, a set of learnable structure-specific visual queries observe corresponding structures in a CT image. The resulting observation tokens are contrasted with structure-specific textual features extracted from the accompanying radiology report with a structure-wise image-text contrastive loss.<n>In the second stage, the visual structure queries are frozen and used to select the critical image patch embeddings depicting each anatomical structure, minimizing distractions from irrelevant areas while reducing memory consumption.
  arXiv  Detail & Related papers  (2026-03-05T07:07:07Z)
• Staged Voxel-Level Deep Reinforcement Learning for 3D Medical Image Segmentation with Noisy Annotations [4.581671524490035]
  We propose an end-to-end Staged Voxel-Level Deep Reinforcement Learning framework for robust medical image segmentation under noisy annotations.<n>This framework employs a dynamic iterative update strategy to automatically mitigate the impact of erroneous labels without requiring manual intervention.
  arXiv  Detail & Related papers  (2026-01-07T12:39:54Z)
• SwinTF3D: A Lightweight Multimodal Fusion Approach for Text-Guided 3D Medical Image Segmentation [0.30586855806896035]
  We propose SwinTF3D, a lightweight multimodal fusion approach that unifies visual and linguistic representations for text-guided 3D medical image segmentation.<n>SwinTF3D achieves competitive Dice and IoU scores across multiple organs, despite its compact architecture.
  arXiv  Detail & Related papers  (2025-12-28T11:00:05Z)
• TGC-Net: A Structure-Aware and Semantically-Aligned Framework for Text-Guided Medical Image Segmentation [56.09179939570486]
  We propose TGC-Net, a CLIP-based framework focusing on parameter-efficient, task-specific adaptations.<n>TGC-Net achieves state-of-the-art performance with substantially fewer trainable parameters, including notable Dice gains on challenging benchmarks.
  arXiv  Detail & Related papers  (2025-12-24T12:06:26Z)
• Text-driven Multiplanar Visual Interaction for Semi-supervised Medical Image Segmentation [48.76848912120607]
  Semi-supervised medical image segmentation is a crucial technique for alleviating the high cost of data annotation.<n>We propose a novel text-driven multiplanar visual interaction framework for semi-supervised medical image segmentation (termed Text-SemiSeg)<n>Our framework consists of three main modules: Text-enhanced Multiplanar Representation (TMR), Category-aware Semantic Alignment (CSA), and Dynamic Cognitive Augmentation (DCA)
  arXiv  Detail & Related papers  (2025-07-16T16:29:30Z)
• TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation [4.034121387622003]
  We propose TAB Net, a novel weakly-supervised medical image segmentation framework.<n>It consists of the triplet augmentation self-recovery (TAS) module and the boundary-aware pseudo-label supervision (BAP) module.<n>We show that TAB Net significantly outperforms state-of-the-art methods for scribble-based weakly supervised segmentation.
  arXiv  Detail & Related papers  (2025-07-03T07:50:00Z)
• SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation [12.168303995947795]
  We propose a novel Semantic-Guided Triplet Co-training framework.<n>It achieves high-end medical image segmentation by only annotating three slices of a few volumetric samples.<n>Our method outperforms most state-of-the-art semi-supervised counterparts under sparse annotation settings.
  arXiv  Detail & Related papers  (2024-12-20T03:31:33Z)
• 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)
• Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation [61.09321488002978]
  We present a novel cascaded robust learning framework for chest X-ray segmentation with imperfect annotation. Our model consists of three independent network, which can effectively learn useful information from the peer networks. Our methods could achieve a significant improvement on the accuracy in segmentation tasks compared to the previous methods.
  arXiv  Detail & Related papers  (2021-04-05T15:50:16Z)
• A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision [62.4773770041279]
  We develop a semi-supervised learning framework based on a teacher-student fashion for organ and lesion segmentation. We show our model is robust to the quality of bounding box and achieves comparable performance compared with full-supervised learning methods.
  arXiv  Detail & Related papers  (2020-10-23T07:58:20Z)

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.