Utnetpara: A Hybrid CNN-Transformer Architecture with Multi-Scale Fusion for Whole-Slide Image Segmentation-Reference-Cited by-同舟云学术

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Utnetpara: A Hybrid CNN-Transformer Architecture with Multi-Scale Fusion for Whole-Slide Image Segmentation

Published:2024-05-27 Issue: Volume:31 Page:1-5
ISSN:
Container-title:2024 IEEE International Symposium on Biomedical Imaging (ISBI)
language:
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Author:

Huang Boqiang¹,Ying Jiayu²,Lyu Ruizhi²,Schaadt Nadine S.³,Klinkhammer Barbara M.⁴,Boor Peter⁴,Lotz Johannes⁵,Feuerhake Friedrich³,Merhof Dorit¹

Affiliation:

1. Institute of Image Analysis and Computer Vision, University of Regensburg,Regensburg,Germany

2. Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany

3. Institute for Pathology, Hannover Medical School,Hannover,Germany

4. Institute of Pathology, RWTH Aachen University Hospital,Aachen,Germany

5. Fraunhofer Institute for Digital Medicine MEVIS,Lübeck,Germany

Funder

Ministry of Education

Deutsche Forschungsgemeinschaft

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635778.pdf?arnumber=10635778

Reference22 articles.

1. U-Net: Convolutional Networks for Biomedical Image Segmentation

2. HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation

3. Contextual Attention Network: Transformer Meets U-Net

4. CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

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