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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 471–480 of 786 papers

TitleStatusHype
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI—0
Segment Anything Model for Brain Tumor Segmentation—0
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging—0
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks—0
Segmentation of brain tumor on magnetic resonance imaging using a convolutional architecture—0
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network—0
Segmentation of Kidney Tumors on Non-Contrast CT Images using Protuberance Detection Network—0
Segmentation of Liver Lesions with Reduced Complexity Deep Models—0
Segmentation of Lung Tumor from CT Images using Deep Supervision—0
Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
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