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TransCeption:Enhancing medical image segmentation with an inception-like transformer design for efficient feature fusion

Reza Azad Yiwei Jia Ehsan Khodapanah Aghdam Julien Cohen-Adad Dorit Merhof

Computational Visual Media2025，Vol.11Issue(5)：P.1079-1095,17.

Computational Visual Media2025，Vol.11Issue(5)：P.1079-1095,17.DOI:10.26599/CVM.2025.9450407

TransCeption:Enhancing medical image segmentation with an inception-like transformer design for efficient feature fusion

Reza Azad ¹Yiwei Jia ¹Ehsan Khodapanah Aghdam ²Julien Cohen-Adad ³Dorit Merhof⁴

作者信息

1. Faculty of Electrical Engineering and Information Technology,RWTH Aachen University,Aachen 52074,Germany
2. Department of Electrical Engineering,Shahid Beheshti University,Tehran 1983969411,Iran
3. NeuroPoly Lab,Institute of Biomedical Engineering,Polytechnique Montreal,Montreal H3T 1J4,Canada Functional Neuroimaging Unit,CRIUGM,University of Montreal,Montreal H3T 1J4,Canada MILA,Quebec AI Institute,Montreal H2S3H1,Canada
4. Faculty of Informatics and Data Science,University of Regensburg,Regensburg 93053,Germany Fraunhofer Institute for Digital Medicine MEVIS,Bremen 28359,Germany.
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摘要

关键词

transformer/medical image segmentation/multi-scale feature fusion/inception

分类

信息技术与安全科学

引用本文复制引用

Reza Azad,Yiwei Jia,Ehsan Khodapanah Aghdam,Julien Cohen-Adad,Dorit Merhof..TransCeption:Enhancing medical image segmentation with an inception-like transformer design for efficient feature fusion[J].Computational Visual Media,2025,11(5):P.1079-1095,17.

基金项目

funded by the German Research Foundation(Deutsche Forschungsgemeinschaft,DFG)project number 455548460. （Deutsche Forschungsgemeinschaft,DFG）

Computational Visual Media

OACSCD

ISSN：2096-0433

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