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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 41–50 of 786 papers

TitleStatusHype
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
3D Self-Supervised Methods for Medical ImagingCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion ModelsCode1
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
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