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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 181–190 of 436 papers

TitleStatusHype
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Brain Tumor Detection Based On Symmetry Information—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
Brain Tumor Segmentation and Survival Prediction—0
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
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