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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 321–330 of 786 papers

TitleStatusHype
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor SegmentationCode0
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks—0
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
GuideGen: A Text-Guided Framework for Full-torso Anatomy and CT Volume GenerationCode0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
A Segmentation Foundation Model for Diverse-type Tumors—0
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation—0
Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality imagesCode0
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy—0
Tumor segmentation on whole slide images: training or prompting?—0
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