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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 401–410 of 436 papers

TitleStatusHype
Consistent estimation of the max-flow problem: Towards unsupervised image segmentation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Deep Recurrent Level Set for Segmenting Brain Tumors—0
Glioma Segmentation with Cascaded UnetCode0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
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