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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 141–150 of 786 papers

TitleStatusHype
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image SegmentationCode1
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor SegmentationCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
Modality-aware Mutual Learning for Multi-modal Medical Image SegmentationCode1
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation NetworkCode1
Learning from partially labeled data for multi-organ and tumor segmentationCode1
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