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

Stenosis

书目名称Kidney and Kidney Tumor Segmentation影响因子(影响力)




书目名称Kidney and Kidney Tumor Segmentation影响因子(影响力)学科排名




书目名称Kidney and Kidney Tumor Segmentation网络公开度




书目名称Kidney and Kidney Tumor Segmentation网络公开度学科排名




书目名称Kidney and Kidney Tumor Segmentation被引频次




书目名称Kidney and Kidney Tumor Segmentation被引频次学科排名




书目名称Kidney and Kidney Tumor Segmentation年度引用




书目名称Kidney and Kidney Tumor Segmentation年度引用学科排名




书目名称Kidney and Kidney Tumor Segmentation读者反馈




书目名称Kidney and Kidney Tumor Segmentation读者反馈学科排名

VEIL

NUL

,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

CHARM

,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

不开心

,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

Lyme-disease

笨拙处理

notice

STERN

,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

Subjugate

,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