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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 191–200 of 436 papers

TitleStatusHype
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
A Structural Graph-Based Method for MRI Analysis—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Survival Prediction using Radiomics Features—0
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—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
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
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