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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 541–550 of 786 papers

TitleStatusHype
Belief function-based semi-supervised learning for brain tumor segmentation—0
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
Glioblastoma Multiforme Patient Survival Prediction—0
Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative StudyCode0
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma—0
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology—0
A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation—0
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture—0
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
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