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Kidney and Kidney Tumor Segmentation: Miccai 2023 Challenge, Kits 2023, Held in Conjunction with Miccai 2023, Vancouver, Bc, Canada, October 8, 2023,

Kidney and Kidney Tumor Segmentation: Miccai 2023 Challenge, Kits 2023, Held in Conjunction with Miccai 2023, Vancouver, Bc, Canada, October 8, 2023, (Paperback, 2024)

Andrew Wood, Nicholas Heller, Fabian Isensee (엮은이)
Springer
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Kidney and Kidney Tumor Segmentation: Miccai 2023 Challenge, Kits 2023, Held in Conjunction with Miccai 2023, Vancouver, Bc, Canada, October 8, 2023,
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책 정보

· 제목 : Kidney and Kidney Tumor Segmentation: Miccai 2023 Challenge, Kits 2023, Held in Conjunction with Miccai 2023, Vancouver, Bc, Canada, October 8, 2023, (Paperback, 2024) 
· 분류 : 외국도서 > 컴퓨터 > 컴퓨터 비전/패턴 인식
· ISBN : 9783031548055
· 쪽수 : 164쪽
· 출판일 : 2024-03-07

목차

Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023.- Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge.- Dynamic resolution network for kidney tumor segmentation.- Analyzing domain shift when using additional data for the MICCAI KiTS23 Challenge.- A Hybrid Network based on nnU-net and Swin Transformer for Kidney Tumor Segmentation.- Leveraging Uncertainty Estimation for Segmentation of Kidney, Kidney Tumor and Kidney Cysts.- An Ensemble of 2.5D ResUnet Based Models for Segmentation of Kidney and Masses.- Using Uncertainty Information for Kidney Tumor Segmentation.- Two-Stage Segmentation and Ensemble Modeling: Kidney Tumor Analysis in CT Images.- GSCA-Net: A global spatial channel attention network for kidney, tumor and cyst segmentation.- Genetic Algorithm enhanced nnU-Net for the MICCAI KiTS23 Challenge.- Two-Stage Segmentation Framework with Parallel Decoders for the Kidney and Kidney Tumor Segmentation.- 3d U-Net with ROI Segmentation of Kidneys and Masses in CT Scans.- Deep Learning-Based Hierarchical Delineation of Kidneys, Tumors, and Cysts in CT Images.- Cascade UNets for Kidney and Kidney Tumor Segmentation.- Cascaded nnU-Net for Kidney and Kidney Tumor Segmentation.- A Deep Learning Approach for the Segmentation of Kidney, Tumor and Cyst in Computed Tomography Scans.- Recursive learning reinforced by redefining the train and validation volumes of an Encoder-Decoder segmentation model.- Attention U-net for Kidney and Masses.- Advancing Kidney, Kidney Tumor, Cyst Segmentation: A Multi-Planner U-Net Approach for the KiTS23 Challenge.- 3D Segmentation of Kidneys, Kidney Tumors and Cysts on CT Images - KiTS23 Challenge.- Kidney and Kidney Tumor Segmentation via Transfer Learning.

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