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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 11–20 of 786 papers

TitleStatusHype
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysisCode2
U-Mamba: Enhancing Long-range Dependency for Biomedical Image SegmentationCode2
Vision Foundation Models for Computed TomographyCode2
Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT ImagesCode2
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)Code2
Conditional Diffusion Models for Semantic 3D Brain MRI SynthesisCode2
ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly SegmentationCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
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