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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 301–310 of 786 papers

TitleStatusHype
Continual Learning for Abdominal Multi-Organ and Tumor SegmentationCode1
Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)—0
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI—0
Conditional Diffusion Models for Semantic 3D Brain MRI SynthesisCode2
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)Code2
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images—0
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)—0
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