Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, Brainles 2020, Held in Conjunction with Micc (Paperback, 2021)

Brain Tumor Segmentation.- Lightweight U-Nets for Brain Tumor Segmentation.- Efficient Brain Tumour Segmentation using Co-registered Data and Ensembles of Specialised Learners.- Efficient MRI Brain Tumor Segmentation using Multi-Resolution Encoder-Decoder Networks.- Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework.- HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation.- H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task.- 2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation.- Attention U-Net with Dimension-hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation.- MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation.- Glioma Segmentation with 3D U-Net Backed with Energy- Based Post- Processing.- nnU-Net for Brain Tumor Segmentation.- A Deep Random Forest Approach for Multimodal Brain Tumor Segmentation.- Brain tumor segmentation and associated uncertainty evaluation using Multi-sequences MRI Mixture Data Preprocessing.- A Deep supervision CNN network for Brain tumor Segmentation.- Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans.- Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation.- Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion.- Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge.- 3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction.- Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI using Selective Kernel Networks.- 3D brain tumor segmentation and survival prediction using ensembles of Convolutional Neural Networks.- Brain Tumour Segmentation using Probabilistic U-Net.- Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets.- A Deep Supervised U-Attention Net for Pixel-wise Brain Tumor Segmentation.- A two stage atrous convolution neural network for brain tumor segmentation.- TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI data.- Brain Tumor Segmentation and Survival Prediction using Automatic Hardmining in 3D CNN Architecture.- Some New Tricks for Deep Glioma Segmentation.- PieceNet: A Redundant UNet Ensemble.- Cerberus: A Multi-headed Network for BrainTumor Segmentation.- An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients based on Volumetric and Shape Features.- Squeeze-and-Excitation Normalization for Brain Tumor Segmentation.- Modified MobileNet for Patient Survival Prediction.- Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation.- Brain Tumor Segmentation and Survival Prediction Using Patch Based Modi?ed U-Net.- DR-Unet104 for Multimodal MRI brain tumor  segmentation.- Glioma Sub-region Segmentation on Multi-parameter MRI with Label Dropout.- Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation.- Learning Dynamic Convolutions for Multi-Modal 3D MRI Brain Tumor Segmentation.- Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification.- Automatic Glioma Grading Based on Two-stage Networks by Integrating Pathology and MRI Images.- Brain Tumor Classification Based on MRI Images and Noise Reduced Pathology Images.- Multimodal brain tumor classification.- A Hybrid Convolutional Neural Network Based-Method for Brain Tumor Classification Using mMRI and WSI.- CNN-based Fully Automatic Glioma Classification with Multi-modal Medical Images.- Glioma Classification Using Multimodal Radiology and Histology Data.