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Record Nr. |
UNISA996466192903316 |
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Titolo |
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 [[electronic resource] ] : 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I / / edited by Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XXXVII, 819 p. 345 illus., 294 illus. in color.) |
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Collana |
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Image Processing, Computer Vision, Pattern Recognition, and Graphics ; ; 11764 |
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Disciplina |
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Soggetti |
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Optical data processing |
Pattern recognition |
Artificial intelligence |
Health informatics |
Image Processing and Computer Vision |
Pattern Recognition |
Artificial Intelligence |
Health Informatics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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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- |
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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 |
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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. |
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Sommario/riassunto |
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The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted |
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Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging. |
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Record Nr. |
UNISA996464518703316 |
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Titolo |
Deep generative models, and data augmentation, labelling, and imperfections : first Workshop, DGM4MICCAI 2021, and first Workshop, DALI 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, proceedings / / Sandy Engelhardt [and nine others] (editors) |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (285 pages) |
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Collana |
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Lecture notes in computer science : image processing, computer vision, pattern recognition, and graphics ; ; Volume 13003 |
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Disciplina |
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Soggetti |
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Diagnostic imaging - Data processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- DGM4MICCAI 2021 Preface -- DGM4MICCAI 2021 Organization -- DALI 2021 Preface -- DALI 2021 Organization -- Contents -- Image-to-Image Translation, Synthesis -- Frequency-Supervised MR-to-CT Image Synthesis -- 1 Introduction -- 2 Method -- 2.1 |
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Frequency-Supervised Synthesis Network -- 2.2 High-Frequency Adversarial Learning -- 3 Experiments and Results -- 3.1 Experimental Setup -- 3.2 Results -- 4 Conclusion -- References -- Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain -- 1 Introduction -- 2 Methods -- 2.1 Style Encoder -- 2.2 Content Encoder -- 2.3 Decoder -- 2.4 Loss Functions -- 2.5 Implementation Details -- 3 Experiments -- 3.1 Qualitative Results -- 3.2 Quantitative Results -- 4 Conclusion -- References -- 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images -- 1 Introduction -- 2 Methods -- 2.1 3D-StyleGAN -- 3 Results -- 4 Discussion -- References -- Bridging the Gap Between Paired and Unpaired Medical Image Translation -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Comparison with Baselines -- 3.2 Ablation Studies -- 4 Conclusion -- References -- Conditional Generation of Medical Images via Disentangled Adversarial Inference -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Dual Adversarial Inference (DAI) -- 2.3 Disentanglement Constrains -- 3 Experiments -- 3.1 Generation Evaluation -- 3.2 Style-Content Disentanglement -- 3.3 Ablation Studies -- 4 Conclusion -- A Disentanglement Constrains -- A.1 Content-Style Information Minimization -- A.2 Self-supervised Regularization -- B Implementation Details -- B.1 Implementation Details -- B.2 Generating Hybrid Images -- C Datasets -- C.1 HAM10000 -- C.2 LIDC -- D Baselines -- D.1 Conditional InfoGAN -- D.2 cAVAE -- D.3 Evaluation Metrics -- E Related Work. |
E.1 Connection to Other Conditional GANs in Medical Imaging -- E.2 Disentangled Representation Learning -- References -- CT-SGAN: Computed Tomography Synthesis GAN -- 1 Introduction -- 2 Methods -- 3 Datasets and Experimental Design -- 3.1 Dataset Preparation -- 4 Results and Discussion -- 4.1 Qualitative Evaluation -- 4.2 Quantitative Evaluation -- 5 Conclusions -- A Sample Synthetic CT-scans from CT-SGAN -- B Nodule Injector and Eraser -- References -- Applications and Evaluation -- Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference -- 1 Introduction -- 2 Methods -- 2.1 Learning -- 2.2 Inference -- 2.3 Objectives -- 2.4 Implementation -- 3 Experiments -- 3.1 Inpainting on Live-Pig Images -- 3.2 Filling in Artifact Regions After Segmentation -- 3.3 Needle Tracking -- 4 Conclusion -- References -- CaCL: Class-Aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns -- 1 Introduction -- 2 Methods -- 2.1 Class-Aware Codebook Based Feature Encoding -- 2.2 Loss Definition -- 2.3 Training Strategy -- 2.4 Weakly Supervised Learning Segmentation -- 3 Data and Experiments -- 4 Results -- 5 Discussion -- References -- BrainNetGAN: Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dementia Classification -- 1 Introduction -- 1.1 Related Work -- 1.2 BrainNetGAN -- 2 Methods -- 2.1 Structural Brain Networks -- 2.2 Data Augmentation Using BrainNetGAN -- 2.3 Data Acquisition and Experimental Setup -- 3 Numerical Results -- 4 Discussion and Conclusion -- References -- Evaluating GANs in Medical Imaging -- 1 Introduction -- 2 Methods -- 2.1 Competing GANs -- 3 Materials -- 4 Experimental Results -- 5 Conclusions -- References -- AdaptOR Challenge -- Improved Heatmap-Based Landmark Detection -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Set. |
2.2 Outline of the Proposed Method -- 2.3 Pre-processing -- 2.4 Point Detection -- 2.5 Post-processing -- 2.6 Evaluation -- 3 Results -- 4 Conclusions -- References -- Cross-Domain Landmarks Detection in Mitral Regurgitation -- 1 Introduction -- 2 Method -- 2.1 Generating |
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Heatmap of Key Points for Training -- 2.2 Inference Procedure -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- DALI 2021 -- Scalable Semi-supervised Landmark Localization for X-ray Images Using Few-Shot Deep Adaptive Graph -- 1 Introduction -- 2 Method -- 2.1 Deep Adaptive Graph -- 2.2 Few-Shot DAG -- 3 Results -- 4 Conclusion -- References -- Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency -- 1 Introduction -- 2 Methodology -- 2.1 Dataset -- 2.2 Methods -- 3 Experiments -- 3.1 Comparative Results -- 3.2 Ablation Study -- 4 Conclusion -- References -- One-Shot Learning for Landmarks Detection -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Offline One-Shot CNN Training -- 2.3 Online Structure Detection -- 2.4 Online Image Patch Registration -- 3 Experiment -- 3.1 Dataset -- 3.2 Network Architecture and Training Details -- 4 Results -- 5 Conclusion -- References -- Compound Figure Separation of Biomedical Images with Side Loss -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Anchor Based Detection -- 3.2 Compound Figure Simulation -- 3.3 Side Loss for Compound Figure Separation -- 4 Data and Implementation Details -- 5 Results -- 5.1 Ablation Study -- 5.2 Comparison with State-of-the-Art -- 6 Conclusion -- References -- Data Augmentation with Variational Autoencoders and Manifold Sampling -- 1 Introduction -- 2 Variational Autoencoder -- 3 Some Elements on Riemannian Geometry -- 4 The Proposed Method. |
4.1 The Wrapped Normal Distribution -- 4.2 Riemannian Random Walk -- 4.3 Discussion -- 5 Data Augmentation Experiments for Classification -- 5.1 Augmentation Setting -- 5.2 Results -- 6 Conclusion -- References -- Medical Image Segmentation with Imperfect 3D Bounding Boxes -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Bounding Box Correction -- 3.2 Bounding Boxes for Weakly Supervised Segmentation -- 3.3 Implementation Details -- 4 Experiments and Discussion -- 4.1 Weakly-Supervised Segmentation of 3D CT Volume Using Bounding Box Correction -- 5 Conclusions and Discussions -- References -- Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images -- 1 Introduction -- 2 Methodology -- 2.1 Cell Segmentation Initialization -- 2.2 Cell-to-Cell Correspondence Using Graph Matching -- 2.3 Data Augmentation Through Iterative Label Transfer -- 2.4 Data Collection and Validation Methods -- 3 Experimental Results -- 3.1 Iterative Cell Segmentation in Noisy Images -- 3.2 Purposeful Data Augmentation Improves Training Results -- 4 Conclusion and Future Work -- References -- How Few Annotations are Needed for Segmentation Using a Multi-planar U-Net? -- 1 Introduction -- 2 Methods -- 3 Datasets -- 4 Experiments and Results -- 5 Discussion -- References -- FS-Net: A New Paradigm of Data Expansion for Medical Image Segmentation -- 1 Introduction -- 2 Proposed FS-Net -- 2.1 Images Channel Coding and Re-Encoding the Ground Truth -- 2.2 FS Module -- 2.3 Weighted Loss -- 3 Experiments -- 3.1 Datasets -- 3.2 Baselines and Implementation -- 3.3 Results -- 3.4 Ablation Study -- 4 Conclusions -- References -- An Efficient Data Strategy for the Detection of Brain Aneurysms from MRA with Deep Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset and Data Annotation -- 2.2 Model Implementation. |
2.3 Patch Generation and Data Augmentation -- 2.4 Metrics and Performance Evaluation -- 3 Experiments and Results -- 3.1 Ablation Study -- 3.2 5-Fold Validation -- 4 Discussion -- 5 Conclusion -- References -- Evaluation of Active Learning Techniques on Medical Image Classification with Unbalanced Data Distributions -- 1 |
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Introduction -- 1.1 Active Learning in Medical Imaging -- 1.2 Active Learning Methodology -- 2 Methods -- 2.1 Datasets -- 2.2 Scoring Functions -- 2.3 Sampling Strategies -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results -- 4 Discussion -- 5 Conclusion -- References -- Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation -- 1 Introduction -- 2 Materials and Methods -- 2.1 Filtered Back-Projection Augmentation -- 2.2 Comparison Augmentation Approaches -- 2.3 Datasets -- 2.4 Quality Metrics -- 3 Experiments -- 3.1 Experimental Pipeline -- 3.2 Network Architecture and Training Setup -- 4 Results -- 5 Conclusion -- References -- Label Noise in Segmentation Networks: Mitigation Must Deal with Bias -- 1 Introduction -- 2 Segmentation Models -- 3 Model Performance on Corrupted Labels -- 3.1 Random Warp -- 3.2 Constant Shift -- 3.3 Random Crop -- 3.4 Permutation -- 4 Limitations and Future Work -- 5 Conclusion -- References -- DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Results and Discussion -- 5 Conclusion -- References -- MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation -- 1 Introduction -- 2 MetaHistoSeg Framework -- 2.1 Histopathology Task Dataset Preprocessing -- 2.2 Task and Instance Level Batch Sampling -- 2.3 Task-Specific Heads and Multi-GPU Support -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Results -- 4 Conclusions -- References -- Author Index. |
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