Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with Miccai 2019, Shenzhen, China, October 13, 2019,

ISBN-13: 9783030326913 / Angielski / Miękka / 2019 / 695 str.

Heung-Il Suk; Mingxia Liu; Pingkun Yan

Spis treści

rain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization.- Spatial Regularized Classification Network for Spinal Dislocation Diagnosis.- Globally-Aware Multiple Instance Classifier for Breast Cancer Screening.- Advancing Pancreas Segmentation in Multi-protocol MRI Volumes using Hausdorff-Sine Loss Function.- WSI-Net: Branch-based and Hierarchy-aware Network for Segmentation and Classification of Breast Histopathological Whole-slide Images.- Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information.- MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network.- DCCL: A Benchmark for Cervical Cytology Analysis.- Smartphone-Supported Malaria Diagnosis Based on Deep Learning.- Children's Neuroblastoma Segmentation using Morphological Features.- GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for automatic detection of esophageal abnormalities in endoscopic images.- Deep Active Lesion Segmentation.- Infant Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning.- A Relation Hashing Network Embedded with Prior Features for Skin Lesion Classification.- End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation.- Privacy-preserving Federated Brain Tumour Segmentation.- Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Histology Images.- Semi-Supervised Multi-Task Learning With Chest X-Ray Images.- Novel Bi-directional Images Synthesis based on WGAN-GP with GMM-based Noise Generation.- Pseudo-labeled bootstrapping and multi-stage transfer learning for the classification and localization of dysplasia in Barrett’s Esophagus.- Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images.- Boundary Aware Networks for Medical Image Segmentation.- Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks.- Morphological Simplification of Brain MR Images by Deep Learning for Facilitating Deformable Registration.- Joint Shape Representation and Classification for Detecting PDAC.- FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans.- Weakly supervised segmentation by a deep geodesic prior.- Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks.- Correspondence-Steered Volumetric Descriptor Learning Using Deep Functional Maps.- Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI.- Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation.- Automatic Couinaud Segmentation from CT Volumes on Liver Using GLC-Unet.- Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images.- Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation.- Detecting abnormalities in resting-state dynamics: An unsupervised learning approach.- Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection.- Dense-residual Attention Network for Skin Lesion Segmentation.- Confounder-Aware Visualization of ConvNets.- Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks.- Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection.- Unsupervised Lesion Detection with Locally Gaussian Approximation.- A Hybrid Multi-atrous and Multi-scale Network for Liver Lesion Detection.- BOLD fMRI-based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks.- Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI.- Unsupervised Conditional Consensus Adversarial Network for Brain Disease Identification with Structural MRI.- A Maximum Entropy Deep Reinforcement Learning Neural Tracker.- Weakly Supervised Confidence Learning for Brain MR Image Dense Parcellation.- Select, Attend, and Transfer: Light, Learnable Skip Connections.- Learning-based Bone Quality Classification Method for Spinal Metastasis.- Automated Segmentation of Skin Lesion Based on Pyramid Attention Network.- Relu cascade of feature pyramid networks for CT pulmonary nodule detection.- Joint Localization of Optic Disc and Fovea in Ultra-Widefield Fundus Images.- Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures.- Lesion Detection by Efficiently Bridging 3D Context.- Communal Domain Learning for Registration in Drifted Image Spaces.- Conv2Warp: An unsupervised deformable image registration with continuous convolution and warping.- Semantic filtering through deep source separation on microscopy images.- Adaptive Functional Connectivity Network using Parallel Hierarchical BiLSTM for MCI Diagnosis.- Multi-Template based Auto-weighted Adaptive Structural Learning for ASD Diagnosis.- Learn to Step-wise Focus on Targets for Biomedical Image Segmentation.- Renal Cell Carcinoma Staging with Learnable Image Histogram-based Deep Neural Network.- Weakly Supervised Learning Strategy for Lung Defect Segmentation.- Gated Recurrent Neural Networks for Accelerated  Ventilation MRI.- A Cascaded Multi-Modality Analysis in Mild Cognitive Impairment.- Deep Residual Learning for Instrument Segmentation in Robotic Surgery.- Deep learning model integrating dilated convolution and deep supervision for brain tumor segmentation in multi-parametric MRI.- A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs.- Automatic Fetal Brain Extraction Using Multi-Stage U-Net with Deep Supervision.- Cross-Modal Attention-Guided Convolutional Network for Multi-Modal Cardiac Segmentation.- High- and Low-Level Feature Enhancement for Medical Image Segmentation.- Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation.- An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis.- Tree-LSTM: Using LSTM to Encode Memory in Anatomical Tree Prediction from 3D Images.- FAIM-A ConvNet Method for Unsupervised 3D Medical Image Registration.- Functional data and long short-term memory networks for diagnosis of Parkinson's Disease.- Joint Holographic Detection and Reconstruction.- Reinforced Transformer for Medical Image Captioning.- Multi Task Convolutional Neural Network for Joint Bone Age Assessment and Ossification Center Detection from Hand Radiograph.