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Tumor Segmentation

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

Papers

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Showing 661–670 of 786 papers

TitleStatusHype
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes—0
Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Segmentation and Survival Prediction—0
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans—0
3D U-Net Based Brain Tumor Segmentation and Survival Days PredictionCode0
3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks—0
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Prediction of Overall Survival of Brain Tumor Patients—0
Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation—0
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