Kidney and Kidney Tumor Segmentation (Record no. 87617)
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| fixed length control field | 05254nam a22006375i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-54806-2 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730171501.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240306s2024 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031548062 |
| -- | 978-3-031-54806-2 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006 |
| 245 10 - TITLE STATEMENT | |
| Title | Kidney and Kidney Tumor Segmentation |
| Sub Title | MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | X, 164 p. 76 illus., 73 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Lecture Notes in Computer Science, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This book constitutes the Third International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2023, which was held in conjunction with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023. The challenge took place in Vancouver, BC, Canada, on October 8, 2023. The 22 contributions presented in this book were carefully reviewed and selected from 29 submissions. This challenge aims to develop the best system for automatic semantic segmentation of kidneys, renal tumors and renal cysts. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Digital techniques. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Heller, Nicholas. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Wood, Andrew. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Isensee, Fabian. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Rädsch, Tim. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Teipaul, Resha. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Papanikolopoulos, Nikolaos. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Weight, Christopher. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-54806-2 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer Nature Switzerland : |
| -- | Imprint: Springer, |
| -- | 2024. |
| 336 ## - | |
| -- | text |
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| -- | computer |
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| -- | rdamedia |
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| -- | online resource |
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| -- | text file |
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| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Image processing |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer vision. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Application software. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer and Information Systems Applications. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1611-3349 ; |
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