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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 241–250 of 786 papers

TitleStatusHype
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival PredictionCode0
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT ImagesCode0
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor SegmentationCode0
Glioblastoma Tumor Segmentation using an Ensemble of Vision TransformersCode0
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival PredictionCode0
Glioma Segmentation with Cascaded UnetCode0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Brain Tumor Detection using Convolutional Neural NetworkCode0
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck CancersCode0
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor SegmentationCode0
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