ORIGINAL RESEARCH article
Front. Med.
Sec. Dermatology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1661984
This article is part of the Research TopicType 2 Inflammatory Skin Diseases: Novel Therapies and Clinical InsightsView all articles
Multi-interactive Feature Embedding Learning for Medical Image Segmentation
Provisionally accepted- Chengdu University of Traditional Chinese Medicine, Chengdu, China
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Medical image segmentation task can provide the lesion object semantic information, but ignores edge texture details from the lesion region. Conversely, the medical image reconstruction task furnishes the object detailed information to facilitate the semantic segmentation through self-supervised learning. The two tasks are supplementary to each other. Therefore, we propose a multi-interactive feature embedding learning for medical image segmentation. In the medical image reconstruction task, we aim to generate the detailed feature representations containing rich textures, edges, and structures, thus bridging the low-level details lost from segmentation features. In particular, we propose an adaptive feature modulation module to efficiently aggregate foreground and background features to obtain a comprehensive feature representation. In the medical segmentation task, we propose a bi-directional fusion module fusing all important complementary information between the two tasks. Besides, we introduce a multi-branch visual mamba to capture structural information at different scales, thus enhancing model adaptation to different lesion regions. Extensive experiments on four datasets demonstrate the effectiveness of our framework.
Keywords: Medical image segmentation, Self-supervised learning, Adaptive feature modulation module, Bi-directional fusion module, Multi-branch vision mamba
Received: 08 Jul 2025; Accepted: 02 Sep 2025.
Copyright: © 2025 Huang and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yijia Huang, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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