11125nam 2200493 450 99646451870331620220627185631.03-030-88210-1(CKB)4100000012037949(MiAaPQ)EBC6737964(Au-PeEL)EBL6737964(OCoLC)1272992911(PPN)258052007(EXLCZ)99410000001203794920220627d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep 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)Cham, Switzerland :Springer,[2021]©20211 online resource (285 pages)Lecture notes in computer science : image processing, computer vision, pattern recognition, and graphics ;Volume 130033-030-88209-8 Includes bibliographical references and index.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 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 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 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.LNCS sublibrary.SL 6,Image processing, computer vision, pattern recognition, and graphics ;Volume 13003.Diagnostic imagingData processingCongressesDiagnostic imagingData processing616.07540285Engelhardt SandyMiAaPQMiAaPQMiAaPQBOOK996464518703316Deep generative models, and data augmentation, labelling, and imperfections2894464UNISA