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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
A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor SegmentationCode0
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation—0
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
Brain MRI Tumor Segmentation with Adversarial Networks—0
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—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
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