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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 261–270 of 436 papers

TitleStatusHype
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation—0
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data—0
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
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