Brain Tumor Segmentation Papers

• Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability
• Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
• Efficient Multi-Scale 3D CNN with fully connected CRF for Accurate Brain Lesion Segmentation
• CANet: Context Aware Network for 3D Brain Tumor Segmentation
• Brain Tumor Segmentation with Deep Neural Networks
• 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI
• Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis
• DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
• Multi-step Cascaded Networks for Brain Tumor Segmentation
• Transfer Learning for Brain Tumor Segmentation
• 3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction
• Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
• Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
• Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
• Lesion Focused Super-Resolution
• 3D MRI brain tumor segmentation using autoencoder regularization
• 3D Self-Supervised Methods for Medical Imaging
• One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation
• Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
• Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks
• Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs
• DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images
• Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
• Autofocus Layer for Semantic Segmentation
• SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
• Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss
• Semi-Supervised Variational Autoencoder for Survival Prediction
• Glioma Segmentation with Cascaded Unet
• Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI
• Knowledge Distillation for Brain Tumor Segmentation