Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation

• URL: http://arxiv.org/abs/2605.14579v1
• Date: Thu, 14 May 2026 08:49:36 GMT
• Title: Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation
• Abstract summary: 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.
• Score: 3.182068203318624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate medical image segmentation is fundamental to precision medicine, yet robust delineation remains challenging under heterogeneous appearances, ambiguous boundaries, and large anatomical variability. Similar intensity and texture patterns between targets and surrounding tissues often lead to blurred activations and unreliable separation. We attribute these failures to representation collapse during encoding and insufficient fine grained multi scale decoding. To address these issues, we propose Med DisSeg, a dispersion driven medical image segmentation framework that jointly improves representation learning and anatomical delineation. Med DisSeg combines a lightweight Dispersive Loss with adaptive attention for fine grained structure segmentation. The Dispersive Loss enlarges inter sample margins by treating in batch hidden representations as negative pairs, producing well dispersed and boundary aware embeddings with negligible overhead. Based on these enhanced representations, the encoder strengthens structure sensitive responses, while the decoder performs adaptive multi scale calibration to preserve complementary local texture and global shape information. Extensive experiments on five datasets spanning three imaging modalities demonstrate consistent state of the art performance. Moreover, Med DisSeg achieves competitive results on multi organ CT segmentation, supporting its robustness and cross task applicability.

Related papers

• TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation [16.600598318231288]
  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.
  arXiv  Detail & Related papers  (2026-04-13T02:31:22Z)
• 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)
• MAMBO-NET: Multi-Causal Aware Modeling Backdoor-Intervention Optimization for Medical Image Segmentation Network [51.68708264694361]
  Confusion factors can affect medical images, such as complex anatomical variations and imaging modality limitations.<n>We propose a multi-causal aware modeling backdoor-intervention optimization network for medical image segmentation.<n>Our method significantly reduces the influence of confusion factors, leading to enhanced segmentation accuracy.
  arXiv  Detail & Related papers  (2025-05-28T01:40:10Z)
• 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)
• Self-supervised Semantic Segmentation: Consistency over Transformation [3.485615723221064]
  We propose a novel self-supervised algorithm, textbfS$3$-Net, which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. We leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches.
  arXiv  Detail & Related papers  (2023-08-31T21:28:46Z)
• Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images [50.76668288066681]
  We present a scale-aware super-resolution network to adaptively segment lesions of various sizes from low-resolution medical images. Our proposed network achieved consistent improvements compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-30T14:25:55Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization [0.43012765978447565]
  Cycle-consistent Generative Adversarial Networks have been used to harmonize image sets between a source and target domain. These methods are prone to instability, contrast inversion, intractable manipulation of pathology, and steganographic mappings which limit their reliable adoption in real-world medical imaging. We propose a segmentation-renormalized image translation framework to reduce inter-scanner harmonization while preserving anatomical layout.
  arXiv  Detail & Related papers  (2021-02-11T23:53:51Z)
• 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.