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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 81–90 of 786 papers

TitleStatusHype
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
3D Self-Supervised Methods for Medical ImagingCode1
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)Code1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
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