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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 281–290 of 436 papers

TitleStatusHype
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
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