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

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書目名稱Kidney and Kidney Tumor Segmentation影響因子(影響力)




書目名稱Kidney and Kidney Tumor Segmentation影響因子(影響力)學(xué)科排名




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書目名稱Kidney and Kidney Tumor Segmentation網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Kidney and Kidney Tumor Segmentation被引頻次




書目名稱Kidney and Kidney Tumor Segmentation被引頻次學(xué)科排名




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書目名稱Kidney and Kidney Tumor Segmentation年度引用學(xué)科排名




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書目名稱Kidney and Kidney Tumor Segmentation讀者反饋學(xué)科排名

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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

不開心

,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

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笨拙處理

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