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Brain Tumor Segmentation

Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.

( Image credit: Brain Tumor Segmentation with Deep Neural Networks )

Papers

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Showing 251–260 of 436 papers

TitleStatusHype
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
RobU-Net: a heuristic robust multi-class brain tumor segmentation approaches for MRI scans—0
Robustifying deep networks for image segmentation—0
Robustness of Brain Tumor Segmentation—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
Segment Anything Model for Brain Tumor Segmentation—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 Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
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