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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
Embracing Massive Medical DataCode1
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)Code1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentationCode1
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
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
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
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