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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 21–30 of 786 papers

TitleStatusHype
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion ModelsCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and AnalysisCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
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