Automated kidney tumor segmentation with convolution and transformer network.- Extraction of Kidney Anatomy based on a 3D U-ResNet with Overlap-Tile Strategy.- Modified nnU-Net for the MICCAI KiTS21 Challenge.- 2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst.- Automated Machine Learning algorithm for Kidney, Kidney tumor, Kidney Cyst segmentation in Computed Tomography Scans.- Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net.- Less is More: Contrast Attention assisted U-Net for Kidney, Tumor and Cyst Segmentations.- A Coarse-to-fine Framework for The 2021 Kidney and Kidney Tumor Segmentation Challenge.- Kidney and kidney tumor segmentation using a two-stage cascade framework.- Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT images.- A Two-stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation.- Mixup Augmentation for Kidney and Kidney TumorSegmentation.- Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge.- An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans.- Contrast-Enhanced CT Renal Tumor Segmentation.- A Cascaded 3D Segmentation Model for Renal Enhanced CT Images.- Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT.- A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans.- 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT.- Kidney and Kidney Tumor Segmentation using Spatial and Channel attention enhanced U-Net Transfer Learning for KiTS21 Challenge.
