Medical Image Computing and Computer Assisted Intervention - Miccai 2019: 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceed

ISBN-13: 9783030322380 / Angielski / Miękka / 2019 / 819 str.

Dinggang Shen; Tianming Liu; Terry M. Peters

Spis treści

Optical Imaging.- Enhancing OCT Signal by Fusion of GANs: Improving Statistical Power of Glaucoma Trials.- A Deep Reinforcement Learning Framework for Frame-by-frame Plaque Tracking on Intravascular Optical Coherence Tomography Image.- Multi-Index Optic Disc Quantification via MultiTask Ensemble Learning.- Retinal Abnormalities Recognition Using Regional Multitask Learning.- Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening.- Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces.- 3D Surface-Based Geometric and Topological Quantification of Retinal Microvasculature in OCT-Angiography via Reeb Analysis.- Limited-Angle Diffuse Optical Tomography Image Reconstruction using Deep Learning.- Data-driven Enhancement of Blurry Retinal Images via Generative Adversarial Networks.- Dual Encoding U-Net for Retinal Vessel Segmentation.- A Deep Learning Design for improving Topology Coherence in Blood Vessel Segmentation.- Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation.- Unsupervised Ensemble Strategy for Retinal Vessel Segmentation.- Fully convolutional boundary regression for retina OCT segmentation.- PM-NET: Pyramid Multi-Label Network for Optic Disc and Cup Segmentation.- Biological Age Estimated from Retinal Imaging: A Novel Biomarker of Aging.- Task Adaptive Metric Space for Medium-Shot Medical Image Classification.- Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization.- Deep Multi Label Classification in Affine Subspaces.- Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss.- A Divide-and-Conquer Approach towards Understanding Deep Networks.- Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography.- Active Appearance Model Induced Generative Adversarial Networks for Controlled Data Augmentation.- Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network.- Probabilistic Atlases to Enforce Topological Constraints.- Synapse-Aware Skeleton Generation for Neural Circuits.- Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation.- EDA-Net: Dense Aggregation of Deep and Shallow Information Achieves Quantitative Photoacoustic Blood Oxygenation Imaging Deep in Human Breast.- Fused Detection of Retinal Biomarkers in OCT Volumes.- Vessel-Net: Retinal Vessel Segmentation under Multi-path Supervision.- Ki-GAN: Knowledge Infusion Generative Adversarial Network for Photoacoustic Image Reconstruction in vivo.- Uncertainty guided semisupervised segmentation of retinal layers in OCT images.- Endoscopy.- Triple ANet: Adaptive Abnormal-aware Attention Network for WCE Image Classification.- Selective Feature Aggregation Network with Area-boundary Constraints for Polyp Segmentation.- Deep Sequential Mosaicking of Fetoscopic Videos.- Landmark-guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection.- Multi-View Learning with Feature Level Fusion for Cervical Dysplasia Diagnosis.- Real-time Surface Deformation Recovery from Stereo Videos.- Microscopy.- Rectified Cross-Entropy and Upper Transition Loss for Weakly Supervised Whole Slide Image Classifier.- From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification.- Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes.- Improving Nuclei/Gland Instance Segmentation in Histopathology Images by Full Resolution Neural Network and Spatial Constrained Loss.- Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification.- Cell Tracking with Deep Learning for Cell Detection and Motion Estimation in Low-Frame-Rate.- Accelerated ML-assisted Tumor Detection in High-Resolution Histopathology Images.- Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype.- Pathology-aware deep network visualization and its application in glaucoma image synthesis.- CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels.- ET-Net: A Generic Edge-Attention Guidance Network for Medical Image Segmentation.- Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints.- Diverse Multiple Prediction on Neural Image Reconstruction.- Deep Segmentation-Emendation Model for Gland Instance Segmentation.- Fast and Accurate Electron Microscopy Image Registration with 3D Convolution.- PlacentaNet: Automatic Morphological Characterization of Placenta Photos with Deep Learning.- Deep Multi-Instance Learning for survival prediction from Whole Slide Images.- High-Resolution Diabetic Retinopathy Image Synthesis Manipulated by Grading and Lesions.- Deep Instance-Level Hard Negative Mining Model for Histopathology Images.- Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells.- Patch Transformer for Multi-tagging Whole Slide Histopathology Images.- Pancreatic Cancer Detection in Whole Slide Images Using Noisy Label Annotations.- Encoding histopathological WSIs using GNN for scalable diagnostically relevant regions retrieval.- Local and Global Consistency Regularized Mean Teacher for Semi-supervised Nuclei Classification.- Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer.- Precise Separation of Adjacent Nuclei using a Siamese Neural Network.- PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis.- DeepACE: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks.- Unsupervised Subtyping of Cholangiocarcinoma Using A Deep Clustering Convolutional Autoencoder.- Evidence Localization for Pathology Images using Weakly Supervised Learning.- Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework.- GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy.- IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation.- Weakly Supervised Cell Segmentation in Dense by Propagating from Detection Map.- Understanding Fixation in Fluorescence Microscopy via Robust Non-negative Tensor Factorization, Atlas-based Motion Correction and Functional Statistics.- ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection, and Cell Classification in Multiplex Immunohistochemistry Images.- Multi-task learning of a deep K-nearest neighbour network for histopathological image classification and retrieval.- Multiclass deep active learning for detecting red blood cell subtypes in brightfield microscopy images.- Enhanced Cycle-Consistent Generative Adversarial Network for Color Normalization of H&E Stained Images.- Nuclei Segmentation in Histopathological Images using Two-Stage Learning.- ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths.- CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation.- PseudoEdgeNet: Nuclei Segmentation only with Point Annotations.- Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification.- Progressive Learning for Neuronal Population Reconstruction from Optical Microscopy Images.- Whole-Sample Mapping of Cancerous and Benign Tissue Properties.- Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification.- Fine-Scale Vessel Extraction in Fundus Images by Registration with Fluorescein Angiography.- DME-Net: Diabetic Macular Edema Grading by Auxiliary Task Learning.- Attention Guided Network for Retinal Image Segmentation.- An unsupervised domain adaptation approach to classification of stem cell-derived cardiomyocytes.