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Optimal Res-UNET architecture with deep supervision for tumor segmentation.

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第一作者： Rahman,Maqsood

第一单位： Department of Information Systems, University of Management and Technology, Lahore, Pakistan.

作者： Rahman,Maqsood ^[1] ; Fazeel,Abid ^[2] ; Jawad,Rasheed ^[3] ; Onur,Osman ^[4] ; Shtwai,Alsubai ^[5]

作者单位： Department of Information Systems, University of Management and Technology, Lahore, Pakistan. ^[1] Department of Computer Science and Information Technology, University of Lahore, Lahore, Pakistan. ^[2] Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, Türkiye.;Department of Software Engineering, Istanbul Nisantasi University, Istanbul, Türkiye.;Applied Science Research Center, Applied Science Private University, Amman, Jordan. ^[3] Department of Electrical and Electronics Engineering, Istanbul Topkapi University, Istanbul, Türkiye. ^[4] Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia. ^[5]

关键词 Residual U-Net attention mechanism deep supervision dice loss encoder-decoder networks image segmentation challenges magnetic resonance imaging medical image analysis

DOI 10.3389/fmed.2025.1593016

PMID 40520778

发布时间 2025-06-16

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