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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 51–60 of 786 papers

TitleStatusHype
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image SegmentationCode1
Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image SegmentationCode1
Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient NetworkCode1
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck CancerCode1
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CTCode1
H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis TasksCode1
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
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