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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 91–100 of 436 papers

TitleStatusHype
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image AnalysisCode1
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
A Structural Graph-Based Method for MRI Analysis—0
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures—0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
A Feasibility study for Deep learning based automated brain tumor segmentation using Magnetic Resonance Images—0
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