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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 121–130 of 786 papers

TitleStatusHype
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reportingCode1
Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation TaskCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIsCode1
Scratch Each Other's Back: Incomplete Multi-Modal Brain Tumor Segmentation via Category Aware Group Self-Support LearningCode1
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
Brain Tumor Segmentation with Deep Neural NetworksCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
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