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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 131–140 of 786 papers

TitleStatusHype
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor SegmentationCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 ChallengeCode1
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challengeCode1
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