Evidence map›Paper›PMID 41771973›Full record

ArticleScientific reports2026

CSWin-MDKDNet: cross-shaped window network with multi-dimensional fusion and knowledge distillation for medical image segmentation.

Guanqing Cui, Hongbin Lin, Lijun Sun, Hua Peng, Haiyue Bao, ShiHao Cai, Quan Lan, Jiangzhou Lin, Chenhui Yang

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Guanqing Cui *School of Informatics, Xiamen University, Xiamen, 361005, China.
Hongbin Lin *Department of Cardiac Surgery, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361008, China.
Lijun SunDepartment of Cardiac Surgery, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361008, China.
Hua PengDepartment of Cardiac Surgery, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361008, China.
Haiyue BaoNational Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361005, China.
ShiHao CaiDepartment of Cardiac Surgery, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361008, China.
Quan LanDepartment of Neurology, First Affiliated Hospital of Xiamen University, Xiamen, China. xmdylanquan@163.com.
Jiangzhou LinXiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361008, China. 13656006475@163.com.
Chenhui YangSchool of Informatics, Xiamen University, Xiamen, 361005, China. chyang@xmu.edu.cn.

Funding

Fujian Provincial Clinical Research Center for Brain Diseases 2021FJSLCYX01the National Natural Science Foundation of China 32401049the Xiamen Medical and Health Guidance Project 3502Z20224ZD1315the Xiamen Natural Science Foundation 3502Z20227415
6 · The paper itself

Abstract

In recent years, deep learning has achieved significant advancements in medical image segmentation. Medical image segmentation is fundamental to computer-aided diagnosis, yet challenges persist in balancing local detail preservation and global context modeling. This paper proposes CSWin-MDKDNet, a novel Transformer-based architecture enhanced with Multi-dimensional Selective Fusion (MDSF) and Knowledge Distillation Loss (KD-loss). The MDSF module refines multi-scale feature fusion through channel-spatial attention, while KD-loss mitigates feature redundancy in deep layers. Evaluated on the Synapse (multi-organ CT), ACDC(cardiac MRI) and ISIC2018 datasets, our model achieves state-of-the-art performance, with 81.82% DSC (Synapse), 91.76% DSC (ACDC) and 91.64% DSC(ISIC2018), outperforming existing methods in accuracy.

Indexed as

Deep LearningImage Processing, Computer-AssistedAlgorithmsHumansMagnetic Resonance ImagingTomography, X-Ray ComputedCross-shaped window attentionDeep learningKnowledge distillationMedical image segmentationMulti-dimensional feature fusion

Identifiers

PMID41771973
PMCPMC13057317

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