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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 681–690 of 786 papers

TitleStatusHype
Glioblastoma Tumor Segmentation using an Ensemble of Vision TransformersCode0
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor ClassificationCode0
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival PredictionCode0
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival PredictionCode0
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scansCode0
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation NetworkCode0
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation ProblemCode0
Brain Tumor Detection using Convolutional Neural NetworkCode0
Medical Federated Model with Mixture of Personalized and Sharing ComponentsCode0
Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound ImagesCode0
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