Full Description
This two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.*
The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions).
*The workshop and challenges were held virtually.
Contents
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 forMultimodal 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 Modified 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.
