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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 211–220 of 436 papers

TitleStatusHype
Class Balanced PixelNet for Neurological Image Segmentation—0
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalitiesCode1
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image SegmentationCode3
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Translation Consistent Semi-supervised Segmentation for 3D Medical ImagesCode1
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Self Pre-training with Masked Autoencoders for Medical Image Classification and SegmentationCode1
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
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