Medical image computing and computer assisted intervention -- MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4-8, 2020, proceedings
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書誌事項
Medical image computing and computer assisted intervention -- MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4-8, 2020, proceedings
（Lecture notes in computer science, 12262 . LNCS sublibrary ; SL 6 . Image processing,
Springer, c2020
- pt. 2
- タイトル別名
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MICCAI 2020
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注記
"The conference was held through a virtual conference management platform, consisting of the main scientific program in addition to featuring 25 work-shops, 8 tutorials, and 24 challenges during October 4-8, 2020."-- Preface
Other editors: Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Includes bibliographical references and author index
内容説明・目次
内容説明
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: machine learning methodologies
Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks
Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis
Part IV: segmentation; shape models and landmark detection
Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology
Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging
Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
目次
Image Reconstruction.- Improving Amide Proton Transfer-weighted MRI Reconstruction using T2-weighted Images.- Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations.- Active MR k-space Sampling with Reinforcement Learning.- Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts.- Joint reconstruction and bias field correction for undersampled MR imaging.- Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping.- End-to-End Variational Networks for Accelerated MRI Reconstruction.- 3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning.- MRI Image Reconstruction via Learning Optimization Using Neural ODEs.- An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms.- Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images using a GAN.- T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions.- Learned Proximal Networks for Quantitative Susceptibility Mapping.- Learning A Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction.- Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography.- Acceleration of High-resolution 3D MR Fingerprinting via a Graph Convolutional Network.- Deep Attentive Wasserstein Generative Adversarial Network for MRI Reconstruction with Recurrent Context-Awareness.- Learning MRI $k$-Space Subsampling Pattern using Progressive Weight Pruning.- Model-driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image.- Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning.- Prediction and Diagnosis.- MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response.- M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients.- Automatic Detection of Free Intra-Abdominal Air in Computed Tomography.- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data.- Geodesically Smoothed Tensor Features for Pulmonary Hypertension Prognosis using the Heart and Surrounding Tissues.- Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution.- DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Dynamic Contrast-Enhanced CT Imaging.- Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions.- Feature-enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon cancer.- Spatial-And-Context aware (SpACe) "virtual biopsy'' radiogenomic maps to target tumor mutational status on structural MRI.- CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis.- Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN.- Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks.- Cross-Domain Methods and Reconstruction.- Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation.- Dynamic memory to alleviate catastrophic forgetting in continuous learning settings.- Unlearning Scanner Bias for MRI Harmonisation.- Cross-Domain Image Translation by Shared Latent Gaussian Mixture Model.- Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy.- X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph.- Domain Adaptation for Ultrasound Beamforming.- CDF-Net: Cross-Domain Fusion Network for accelerated MRI reconstruction.- Domain Adaptation.- Improve Unseen Domain Generalization via Enhanced Local Color Transformation and Augmentation.- Transport-based Joint Distribution Alignment for Multi-site Autism Spectrum Disorder Diagnosis using Resting-state fMRI.- Automatic and interpretable model for periodontitis diagnosis in panoramic radiographs.- Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening.- Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains.- Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes.- Unsupervised Graph Domain Adaptation for Neurodevelopmental Disorders Diagnosis.- JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering.- MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint.- Machine Learning Applications.- Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment.- Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions.- Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect.- Chest X-ray Report Generation through Fine-Grained Label Learning.- Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time.- A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction.- Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications.- Large-scale inference of liver fat with neural networks on UK Biobank body MRI.- BUNET: Blind Medical Image Segmentation Based on Secure UNET.- Temporal-consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape.- Decision Support for Intoxication Prediction Using Graph Convolutional Networks.- Latent-Graph Learning for Disease Prediction.- Generative Adversarial Networks.- BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification.- Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement.- Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN.- GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI.- Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network.- AGAN: An Anatomy Corrector Conditional Generative Adversarial Network.- SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI.- Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation.- Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields.- Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation.- Graded Image Generation Using Stratified CycleGAN.- Prediction of Plantar Shear Stress Distribution by Conditional GAN with Attention Mechanism.
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