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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
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT ImagesCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic SegmentationCode1
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