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_aMedical Image Computing and Computer Assisted Intervention - MICCAI 2020 _h[electronic resource] : _b23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part II / _cedited by Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz. |
| 250 | _a1st ed. 2020. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
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| 300 |
_aXXXVII, 785 p. 258 illus., 228 illus. in color. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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| 490 | 1 |
_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v12262 |
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| 505 | 0 | _aImage 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 ComputerAided 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 MLsolutions -- 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. | |
| 520 | _aThe 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. | ||
| 650 | 0 |
_aComputer vision. _9117455 |
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| 650 | 0 |
_aArtificial intelligence. _93407 |
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| 650 | 0 |
_aSocial sciences _xData processing. _983360 |
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| 650 | 0 |
_aEducation _xData processing. _982607 |
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| 650 | 0 |
_aPattern recognition systems. _93953 |
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| 650 | 0 |
_aBioinformatics. _99561 |
|
| 650 | 1 | 4 |
_aComputer Vision. _9117456 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 650 | 2 | 4 |
_aComputer Application in Social and Behavioral Sciences. _931815 |
| 650 | 2 | 4 |
_aComputers and Education. _941129 |
| 650 | 2 | 4 |
_aAutomated Pattern Recognition. _931568 |
| 650 | 2 | 4 |
_aComputational and Systems Biology. _931619 |
| 700 | 1 |
_aMartel, Anne L. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117457 |
|
| 700 | 1 |
_aAbolmaesumi, Purang. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117458 |
|
| 700 | 1 |
_aStoyanov, Danail. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117459 |
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| 700 | 1 |
_aMateus, Diana. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117460 |
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| 700 | 1 |
_aZuluaga, Maria A. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117461 |
|
| 700 | 1 |
_aZhou, S. Kevin. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117462 |
|
| 700 | 1 |
_aRacoceanu, Daniel. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117463 |
|
| 700 | 1 |
_aJoskowicz, Leo. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117464 |
|
| 710 | 2 |
_aSpringerLink (Online service) _9117465 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030597122 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030597146 |
| 830 | 0 |
_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v12262 _9117466 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-59713-9 |
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