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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 561–570 of 786 papers

TitleStatusHype
Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation—0
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities—0
Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation—0
Brain tumour segmentation using a triplanar ensemble of U-NetsCode0
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
Weakly supervised pan-cancer segmentation tool—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation—0
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