Medical Image Segmentation Review: The Success of U-Net

Author:

Azad Reza1,Aghdam Ehsan Khodapanah2,Rauland Amelie1,Jia Yiwei1,Avval Atlas Haddadi3,Bozorgpour Afshin4,Karimijafarbigloo Sanaz4,Cohen Joseph Paul5,Adeli Ehsan6,Merhof Dorit4

Affiliation:

1. Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany

2. Independent Researcher, Tabriz, Iran

3. School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

4. Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany

5. Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Palo Alto, California, USA

6. Stanford University, Stanford, California, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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4. Leveraging Unlabeled Data for 3D Medical Image Segmentation Through Self-Supervised Contrastive Learning;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27

5. Fusenet: Self-Supervised Dual-Path Network For Medical Image Segmentation;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27

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