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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 621–630 of 786 papers

TitleStatusHype
Soft Tissue Sarcoma Co-Segmentation in Combined MRI and PET/CT Data—0
A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy—0
Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans—0
Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI—0
Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation—0
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation—0
E^2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans—0
Multi-Domain Image Completion for Random Missing Input Data—0
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images—0
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