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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

Showing 141150 of 786 papers

TitleStatusHype
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image SegmentationCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
Embracing Massive Medical DataCode1
Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet)Code1
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT ScansCode1
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image SegmentationCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
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