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Titlebook: Kidney and Kidney Tumor Segmentation; MICCAI 2023 Challeng Nicholas Heller,Andrew Wood,Christopher Weight Conference proceedings 2024 The E

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發(fā)表于 2025-3-21 17:38:53 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Kidney and Kidney Tumor Segmentation
副標(biāo)題MICCAI 2023 Challeng
編輯Nicholas Heller,Andrew Wood,Christopher Weight
視頻videohttp://file.papertrans.cn/543/542691/542691.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Kidney and Kidney Tumor Segmentation; MICCAI 2023 Challeng Nicholas Heller,Andrew Wood,Christopher Weight Conference proceedings 2024 The E
描述.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..
出版日期Conference proceedings 2024
關(guān)鍵詞Computer Science; Informatics; Conference Proceedings; Research; Applications; Computer Vision; Machine Le
版次1
doihttps://doi.org/10.1007/978-3-031-54806-2
isbn_softcover978-3-031-54805-5
isbn_ebook978-3-031-54806-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
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,Dynamic Resolution Network for?Kidney Tumor Segmentation,nd radiological analysis. However, the task is challenging due to the considerable variation in tumor scales between different cases, which is not effectively addressed by conventional segmentation methods. In this paper, we propose a method called dynamic resolution that addresses this issue by dyn
地板
發(fā)表于 2025-3-22 04:47:00 | 只看該作者
,Analyzing Domain Shift When Using Additional Data for?the?MICCAI KiTS23 Challenge,l and the model needs to generalize well from few available data. Unlike transfer learning in which a model pretrained on huge datasets is fine-tuned for a specific task using limited data, we research the case in which we acquire supplementary training material and combine it with the original trai
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發(fā)表于 2025-3-22 09:49:54 | 只看該作者
,A Hybrid Network Based on?nnU-Net and?Swin Transformer for?Kidney Tumor Segmentation,tment of kidney cancer. Deep learning-based automatic medical image segmentation can help to confirm the diagnosis. The traditional 3D nnU-net based on convolutional layers is widely used in medical image segmentation. However, the fixed receptive field of convolutional neural networks introduces an
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,Two-Stage Segmentation and?Ensemble Modeling: Kidney Tumor Analysis in?CT Images,latform, our research introduces a two-stage strategy combining the strengths of nnU-Net and nnFormer for enhanced tumor segmentation. Our approach focuses on the kidney region, facilitating the learning of tumor-influenced areas, and employs an ensemble of two nnU-Net models for precise segmentatio
10#
發(fā)表于 2025-3-23 05:53:29 | 只看該作者
,GSCA-Net: A Global Spatial Channel Attention Network for?Kidney, Tumor and?Cyst Segmentation,chitecture as the pre-processing method to extract the region of interest (ROI) and segment the kidney. Then, we propose Global Spatial Channel Attention Network (GSCA-Net) with global spatial attention (GSA) and global channel attention (GCA) for the segmentation of tumors and cysts. Global spatial
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