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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 111–120 of 786 papers

TitleStatusHype
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic ClassificationCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Medical Image Segmentation Using Squeeze-and-Expansion TransformersCode1
The Federated Tumor Segmentation (FeTS) ChallengeCode1
mlf-core: a framework for deterministic machine learningCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
TransBTS: Multimodal Brain Tumor Segmentation Using TransformerCode1
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
Representation Disentanglement for Multi-modal brain MR AnalysisCode1
Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT ImagesCode1
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