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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 591–600 of 786 papers

TitleStatusHype
Multi-channel MRI Embedding: An EffectiveStrategy for Enhancement of Human Brain WholeTumor SegmentationCode0
Brain Tumor Survival Prediction using Radiomics Features—0
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
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
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
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
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