CoInS-Net: A Continuous Position-Aware Network for Joint Medical Image Interpolation and Segmentation

• URL: http://arxiv.org/abs/2608.09391v1
• Date: Mon, 10 Aug 2026 10:19:16 GMT
• Title: CoInS-Net: A Continuous Position-Aware Network for Joint Medical Image Interpolation and Segmentation
• Abstract summary: We propose a continuous position-continuity interaction network, termed CoInS-Net, for joint frameaware and lesion segmentation.<n>A prototype-based task mutual interaction module lets the segmentation and branches exchange global structure.<n>A multiscale task-cooperative decoder further separates each scale into shared and task-specific components, so the two tasks reinforce common anatomy.
• Score: 8.415835019440769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate medical image interpolation and anatomical structure segmentation are fundamental for computer-aided diagnosis and treatment planning. Anisotropic medical volumes with sparse through-plane sampling often suffer from structural discontinuity and boundary blur, hindering reliable clinical image analysis. Most existing methods implement interpolation and segmentation independently, which introduces redundant computation and fails to fully exploit complementary cross-slice structural information between sequential slices. To address these issues, we propose a continuous position-aware interaction network, termed CoInS-Net, for joint frame interpolation and lesion segmentation. Unlike conventional cascaded interpolation-then-segmentation paradigms, the framework enables bidirectional interaction under a shared Swin encoder with continuous spatial coordinate queries. A spatially continuous position interpolation module generates target-position features at every scale from the relative coordinate and physical spacing, and a prototype-based task mutual interaction module lets the segmentation and interpolation branches exchange global structure through a small set of shared prototypes rather than dense feature mixing. A multi-scale task-cooperative decoder further separates each scale into shared and task-specific components, so the two tasks reinforce common anatomy while preserving their distinct requirements down to the boundary level, without extra annotations. Experiments on four public medical imaging datasets with diverse modalities and anatomical regions demonstrate that the proposed method outperforms conventional single-task schemes. The joint optimization framework effectively realizes mutual promotion between interpolation and segmentation tasks, providing a reliable and universal technical scheme for intelligent clinical medical image analysis.

Related papers

• UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation [44.30877351899412]
  UniMedSeg is a Transformer-centric universal segmentation framework for medical images.<n>It maps visual examples, geometric interactions, language instructions, and 2D/3D images into a shared sequence space.<n>It achieves state-of-the-art performance across visual in-context, interactive, and language-guided segmentation without task-specific fine-tuning.
  arXiv  Detail & Related papers  (2026-07-14T15:34:46Z)
• Towards Voxel Spacing Consistency for Medical Image Segmentation [56.992050146446495]
  We study the correlation between image resampling and segmentation, and propose Consispace, a semantic-aware resampling framework.<n> Consispace achieves consistent voxel spacing in the axial direction while preserving anatomical and semantic consistency.
  arXiv  Detail & Related papers  (2026-06-30T15:42:23Z)
• 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)
• PULSE: A Unified Multi-Task Architecture for Cardiac Segmentation, Diagnosis, and Few-Shot Cross-Modality Clinical Adaptation [0.27998963147546135]
  We introduce PULSE, a multi-task vision-language framework built on self-supervised representations and optimized through a composite supervision strategy.<n>A multi-scale token reconstruction decoder enables anatomical segmentation, while shared global representations support disease classification and clinically grounded text output.<n>Unlike prior task-specific pipelines, PULSE learns task-invariant cardiac priors, generalizes robustly across datasets, and can be adapted to new imaging modalities with minimal supervision.
  arXiv  Detail & Related papers  (2025-12-03T14:49:01Z)
• Co-Seg++: Mutual Prompt-Guided Collaborative Learning for Versatile Medical Segmentation [4.584473085778697]
  Medical image analysis is critical yet challenged by the need of jointly segmenting organs or tissues.<n>Existing studies typically formulated different segmentation tasks in isolation.<n>We propose a Co-Seg++ framework for versatile medical segmentation.
  arXiv  Detail & Related papers  (2025-06-20T17:05:09Z)
• Robust Semi-supervised Multimodal Medical Image Segmentation via Cross Modality Collaboration [21.97457095780378]
  We propose a novel semi-supervised multimodal segmentation framework that is robust to scarce labeled data and misaligned modalities. Our framework employs a novel cross modality collaboration strategy to distill modality-independent knowledge, which is inherently associated with each modality. It also integrates contrastive consistent learning to regulate anatomical structures, facilitating anatomical-wise prediction alignment on unlabeled data.
  arXiv  Detail & Related papers  (2024-08-14T07:34:12Z)
• 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)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation [73.84166499988443]
  We present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA) Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images.
  arXiv  Detail & Related papers  (2020-02-06T13:49:47Z)

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.