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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 641–650 of 786 papers

TitleStatusHype
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
Automatic Brain Tumor Segmentation with Scale Attention NetworkCode0
Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss functionCode0
A New Three-stage Curriculum Learning Approach to Deep Network Based Liver Tumor SegmentationCode0
A New Logic For Pediatric Brain Tumor SegmentationCode0
Post-hoc Overall Survival Time Prediction from Brain MRICode0
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor SegmentationCode0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete ModalitiesCode0
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural NetworksCode0
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