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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 351–375 of 436 papers

TitleStatusHype
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival PredictionCode0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation—0
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation ProblemCode0
Organ At Risk Segmentation with Multiple Modality—0
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
Brain MRI Tumor Segmentation with Adversarial Networks—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Segmentation and Survival Prediction—0
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans—0
3D U-Net Based Brain Tumor Segmentation and Survival Days PredictionCode0
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Prediction of Overall Survival of Brain Tumor Patients—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
Models Genesis: Generic Autodidactic Models for 3D Medical Image AnalysisCode1
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
A Structural Graph-Based Method for MRI Analysis—0
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