Kooperativer Bibliotheksverbund

UID:

Format: XXXIX, 801 p. 271 illus. , online resource.

Edition: 1st ed. 2021.

ISBN: 9783030872342

Series Statement: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12907

Content: The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* 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: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging - others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.

Note: Clinical Applications - Abdomen -- Learning More for Free - A Multi Task Learning Approach for Improved Pathology Classification in Capsule Endoscopy -- Learning-based attenuation quantification in abdominal ultrasound -- Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment -- Non-invasive Assessment of Hepatic Venous Pressure Gradient (HVPG) Based on MR Flow Imaging and Computational Fluid Dynamics -- Deep-Cleansing: Deep-learning based Electronic Cleansing in Dual-energy CT Colonography -- Clinical Applications - Breast -- Interactive smoothing parameter optimization in DBT Reconstruction using Deep learning -- Synthesis of Contrast-enhanced Spectral Mammograms from Low-energy Mammograms Using cGAN-Based Synthesis Network -- Self-adversarial Learning for Detection of Clustered Microcalcifications in Mammograms -- Graph Transformers for Characterization and Interpretation of Surgical Margins -- Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning -- Learned super resolution ultrasound for improved breast lesion characterization -- BI-RADS Classification of Calcification on Mammograms -- Supervised Contrastive Pre-Training for Mammographic Triage Screening Models -- Trainable summarization to improve breast tomosynthesis classification -- Clinical Applications - Dermatology -- Multi-level Relationship Capture Network for Automated Skin Lesion Recognition -- Culprit-Prune-Net: Efficient Continual Sequential Multi-Domain Learning with Application to Skin Lesion Classification -- End-to-end Ugly Duckling Sign Detection for Melanoma Identification with Transformers -- Automatic Severity Rating for Improved Psoriasis Treatment -- Clinical Applications - Fetal Imaging -- STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning -- Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps -- EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography -- AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes -- Learning Spatiotemporal Probabilistic Atlas of Fetal Brains with Anatomically Constrained Registration Network -- Clinical Applications - Lung -- Leveraging Auxiliary Information from EMR for Weakly Supervised Pulmonary Nodule Detection -- M-SEAM-NAM: Multi-instance Self-supervised Equivalent Attention Mechanism with Neighborhood Affinity Module for Double Weakly Supervised Segmentation of COVID-19 -- Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs -- Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning -- RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting -- Detecting when pre-trained nnU-Net models fail silently for Covid-19 lung lesion segmentation -- Perceptual Quality Assessment of Chest Radiograph -- Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-Ray for COVID19 -- Determination of error in 3D CT to 2D fluoroscopy image registration for endobronchial guidance -- Chest Radiograph Disentanglement for COVID-19 Outcome Prediction -- Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms -- LuMiRa: An Integrated Lung Deformation Atlas and 3D-CNN model of Infiltrates for COVID-19 Prognosis -- Clinical Applications - Neuroimaging - Brain Development -- Multi-site Incremental Image Quality Assessment of Structural MRI via Consensus Adversarial Representation Adaptation -- Surface-Guided Image Fusion for Preserving Cortical Details in Human Brain Templates -- Longitudinal Correlation Analysis for Decoding Multi-Modal Brain Development -- ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities -- Covariate Correcting Networks for Identifying Associations between Socioeconomic Factors and Brain Outcomes in Children -- Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images -- Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation -- Joint PVL Detection and Manual Ability Classification using Semi-Supervised Multi-task Learning -- Clinical Applications - Neuroimaging - DWI And Tractography -- Active Cortex Tractography -- Highly Reproducible Whole Brain Parcellation in Individuals via Voxel Annotation with Fiber Clusters -- Accurate parameter estimation in fetal diffusion-weighted MRI - learning from fetal and newborn data -- Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation -- Disentangled and Proportional Representation Learning for Multi-View Brain Connectomes -- Quantifying structural connectivity in brain tumor patients -- Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI -- Clinical Applications - Neuroimaging - Functional Brain Networks -- Detecting Brain State Changes by Geometric Deep Learning of Functional Dynamics on Riemannian Manifold -- From Brain to Body: Learning Low-Frequency Respiration and Cardiac Signals from fMRI Dynamics -- Multi-Head GAGNN: A Multi-Head Guided Attention Graph Neural Network for Modeling Spatio-Temporal Patterns of Holistic Brain Functional Networks -- Building Dynamic Hierarchical Brain Networks and Capturing Transient Meta-states for Early Mild Cognitive Impairment Diagnosis -- Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates -- Efficient neural network approximation of robust PCA for automated analysis of calcium imaging data -- Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query -- Estimation of spontaneous neuronal activity using homomorphic filtering -- A Matrix Auto-encoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes -- Clinical Applications - Neuroimaging - Others -- Topological Receptive Field Model for Human Retinotopic Mapping -- SegRecon: Learning Joint Brain Surface Reconstruction and Segmentation from Images -- LG-Net: Lesion Gate Network for Multiple Sclerosis Lesion Inpainting -- Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging -- SyNCCT: Synthetic Non-Contrast Images of the Brain from Single-Energy Computed Tomography Angiography -- Local Morphological Measures Confirm that Folding within Small Partitions of the Human Cortex Follows Universal Scaling Law -- Exploring the Functional Difference of Gyri/Sulci via Hierarchical Interpretable Autoencoder -- Personalized Matching and Analysis of Cortical Folding Patterns via Patch-Based Intrinsic Brain Mapping -- Clinical Applications - Oncology -- A Location Constrained Dual-branch Network for Reliable Diagnosis of Jaw Tumors and Cysts -- Motion Correction for Liver DCE-MRI with Time-Intensity Curve Constraint -- Parallel Capsule Networks for Classification of White Blood Cells -- Incorporating Isodose Lines and Gradient Information via Multi-task Learning for Dose Prediction in Radiotherapy -- Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining -- Do we need complex image features to personalize treatment of patients with locally advanced rectal cancer? -- Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783030872335

Additional Edition: Printed edition: ISBN 9783030872359

Language: English

DOI: 10.1007/978-3-030-87234-2

URL: https://doi.org/10.1007/978-3-030-87234-2