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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 131–140 of 436 papers

TitleStatusHype
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture—0
Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
Brain Tumor Segmentation and Survival Prediction—0
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
Brain Tumor Segmentation: A Comparative Analysis—0
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