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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 531–540 of 786 papers

TitleStatusHype
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images—0
Representation Disentanglement for Multi-modal brain MR AnalysisCode1
Post-hoc Overall Survival Time Prediction from Brain MRICode0
Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT ImagesCode1
Benefits of Linear Conditioning with Metadata for Image Segmentation—0
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentationCode1
Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation—0
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning-based simulation study—0
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