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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 761–770 of 786 papers

TitleStatusHype
Does anatomical contextual information improve 3D U-Net based brain tumor segmentation?—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency—0
Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning-based simulation study—0
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
TAGS: 3D Tumor-Adaptive Guidance for SAM—0
DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images—0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation—0
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
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