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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 601–610 of 786 papers

TitleStatusHype
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI—0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation—0
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Region of Interest Identification for Brain Tumors in Magnetic Resonance Images—0
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
Relevance analysis of MRI sequences for automatic liver tumor segmentation—0
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net—0
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