Abstract is missing.
- Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 ChallengeAndriy Myronenko, Dong Yang 0005, Yufan He, Daguang Xu. 1-7 [doi]
- Exploring 3D U-Net Training Configurations and Post-processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation ChallengeKwang-Hyun Uhm, Hyunjun Cho, Zhixin Xu, Seohoon Lim, Seung-Won Jung, Sung-Hoo Hong, Sung Jea Ko. 8-13 [doi]
- Dynamic Resolution Network for Kidney Tumor SegmentationShuolin Liu, Bing Han. 14-21 [doi]
- Analyzing Domain Shift When Using Additional Data for the MICCAI KiTS23 ChallengeGeorge Stoica, Mihaela Breaban, Vlad Barbu. 22-29 [doi]
- A Hybrid Network Based on nnU-Net and Swin Transformer for Kidney Tumor SegmentationLifei Qian, Ling Luo, Yuanhong Zhong, Daidi Zhong. 30-39 [doi]
- Leveraging Uncertainty Estimation for Segmentation of Kidney, Kidney Tumor and Kidney CystsZohaib Salahuddin, Sheng Kuang, Philippe Lambin, Henry C. Woodruff. 40-46 [doi]
- An Ensemble of 2.5D ResUnet Based Models for Segmentation of Kidney and MassesCancan Chen, Rongguo Zhang. 47-53 [doi]
- Using Uncertainty Information for Kidney Tumor SegmentationJoffrey Michaud, Tewodros Weldebirhan Arega, Stéphanie Bricq. 54-59 [doi]
- Two-Stage Segmentation and Ensemble Modeling: Kidney Tumor Analysis in CT ImagesSoohyun Lee, HyeYeon Won, Yeeun Lee. 60-66 [doi]
- GSCA-Net: A Global Spatial Channel Attention Network for Kidney, Tumor and Cyst SegmentationXiqing Hu, Yanjun Peng. 67-76 [doi]
- Genetic Algorithm Enhanced nnU-Net for the MICCAI KiTS23 ChallengeTao Li, Di Liu, Bo Yang, Yifan Li, Cheng Zhen. 77-82 [doi]
- Two-Stage Segmentation Framework with Parallel Decoders for the Kidney and Kidney Tumor SegmentationZhengyu Li, Yanjun Peng, Zengmin Zhang. 83-92 [doi]
- 3d U-Net with ROI Segmentation of Kidneys and Masses in CT ScansConnor Mitchell, Shuwei Xing, Aaron Fenster. 93-96 [doi]
- Deep Learning-Based Hierarchical Delineation of Kidneys, Tumors, and Cysts in CT ImagesAndrew Heschl, Hosein Beheshtifard, Phuong-Thao Nguyen, Tapotosh Ghosh, Katie L. Ovens, Farhad Maleki. 97-106 [doi]
- Cascaded nnU-Net for Kidney and Kidney Tumor SegmentationYaqi Wang, Yu Dai 0002, Jianxun Zhang, Jingjing Yin. 114-119 [doi]
- A Deep Learning Approach for the Segmentation of Kidney, Tumor and Cyst in Computed Tomography ScansKartik Narayan Sahoo, Kumaradevan Punithakumar. 120-125 [doi]
- Recursive Learning Reinforced by Redefining the Train and Validation Volumes of an Encoder-Decoder Segmentation ModelAntonio Vispi. 126-138 [doi]
- Attention U-Net for Kidney and MassesDuho Lee, Heeyeon Choi. 139-142 [doi]
- Advancing Kidney, Kidney Tumor, Cyst Segmentation: A Multi-Planner U-Net Approach for the KiTS23 ChallengeSumit Pandey, Toshali, Mathias Perslev, Erik B. Dam. 143-148 [doi]
- 3D Segmentation of Kidneys, Kidney Tumors and Cysts on CT Images - KiTS23 ChallengeMarta Kaczmarska, Karol Majek. 149-155 [doi]
- Kidney and Kidney Tumor Segmentation via Transfer LearningNozadze Giorgi. 156-162 [doi]