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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 51–60 of 786 papers

TitleStatusHype
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT ImagesCode1
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CTCode1
BUSIS: A Benchmark for Breast Ultrasound Image SegmentationCode1
Annotation-efficient deep learning for automatic medical image segmentationCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
A Joint Graph and Image Convolution Network for Automatic Brain Tumor SegmentationCode1
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasetsCode1
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
AutoPET Challenge 2023: Sliding Window-based Optimization of U-NetCode1
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