default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDKiTS@MICCAI 2023: Vancouver, BC, Canada
- Nicholas Heller
, Andrew Wood, Fabian Isensee, Tim Rädsch, Resha Teipaul, Nikolaos Papanikolopoulos, Christopher Weight:
Kidney and Kidney Tumor Segmentation - MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. Lecture Notes in Computer Science 14540, Springer 2024, ISBN 978-3-031-54805-5 - Andriy Myronenko, Dong Yang, Yufan He, Daguang Xu:
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge. 1-7 - Kwang-Hyun Uhm, Hyunjun Cho, Zhixin Xu, Seohoon Lim, Seung-Won Jung, Sung-Hoo Hong, Sung-Jea Ko:
Exploring 3D U-Net Training Configurations and Post-processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge. 8-13 - Shuolin Liu, Bing Han:
Dynamic Resolution Network for Kidney Tumor Segmentation. 14-21 - George Stoica, Mihaela Breaban, Vlad Barbu:
Analyzing Domain Shift When Using Additional Data for the MICCAI KiTS23 Challenge. 22-29 - Lifei Qian, Ling Luo, Yuanhong Zhong, Daidi Zhong:
A Hybrid Network Based on nnU-Net and Swin Transformer for Kidney Tumor Segmentation. 30-39 - Zohaib Salahuddin, Sheng Kuang, Philippe Lambin, Henry C. Woodruff:
Leveraging Uncertainty Estimation for Segmentation of Kidney, Kidney Tumor and Kidney Cysts. 40-46 - Cancan Chen, Rongguo Zhang:
An Ensemble of 2.5D ResUnet Based Models for Segmentation of Kidney and Masses. 47-53 - Joffrey Michaud, Tewodros Weldebirhan Arega, Stéphanie Bricq:
Using Uncertainty Information for Kidney Tumor Segmentation. 54-59 - Soohyun Lee, Hyeyeon Won, Yeeun Lee:
Two-Stage Segmentation and Ensemble Modeling: Kidney Tumor Analysis in CT Images. 60-66 - Xiqing Hu, Yanjun Peng:
GSCA-Net: A Global Spatial Channel Attention Network for Kidney, Tumor and Cyst Segmentation. 67-76 - Tao Li, Di Liu, Bo Yang, Yifan Li, Cheng Zhen:
Genetic Algorithm Enhanced nnU-Net for the MICCAI KiTS23 Challenge. 77-82 - Zhengyu Li, Yanjun Peng, Zengmin Zhang:
Two-Stage Segmentation Framework with Parallel Decoders for the Kidney and Kidney Tumor Segmentation. 83-92 - Connor Mitchell, Shuwei Xing, Aaron Fenster:
3d U-Net with ROI Segmentation of Kidneys and Masses in CT Scans. 93-96 - Andrew Heschl, Hosein Beheshtifard, Phuong Thao Nguyen, Tapotosh Ghosh, Katie L. Ovens, Farhad Maleki:
Deep Learning-Based Hierarchical Delineation of Kidneys, Tumors, and Cysts in CT Images. 97-106 - Konstantinos Koukoutegos, Frederik Maes, Hilde Bosmans:
Cascade UNets for Kidney and Kidney Tumor Segmentation. 107-113 - Yaqi Wang, Yu Dai, Jianxun Zhang, Jingjing Yin:
Cascaded nnU-Net for Kidney and Kidney Tumor Segmentation. 114-119 - Kartik Narayan Sahoo, Kumaradevan Punithakumar:
A Deep Learning Approach for the Segmentation of Kidney, Tumor and Cyst in Computed Tomography Scans. 120-125 - Antonio Vispi:
Recursive Learning Reinforced by Redefining the Train and Validation Volumes of an Encoder-Decoder Segmentation Model. 126-138 - Duho Lee, Heeyeon Choi:
Attention U-Net for Kidney and Masses. 139-142 - Sumit Pandey, Toshali, Mathias Perslev, Erik B. Dam:
Advancing Kidney, Kidney Tumor, Cyst Segmentation: A Multi-Planner U-Net Approach for the KiTS23 Challenge. 143-148 - Marta Kaczmarska, Karol Majek:
3D Segmentation of Kidneys, Kidney Tumors and Cysts on CT Images - KiTS23 Challenge. 149-155 - Nozadze Giorgi:
Kidney and Kidney Tumor Segmentation via Transfer Learning. 156-162
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-02-10 21:32 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
