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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 291–300 of 436 papers

TitleStatusHype
Self-supervised Tumor Segmentation through Layer Decomposition—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma SegmentationCode0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
HMM Model for Brain Tumor Detection and ClassificationCode0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
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