Medical Image Computing and Computer Assisted Intervention ¿ MICCAI 2021

24th International Conference, Strasbourg, France, September 27 ¿ October 1, 2021, Proceedings, Part VII
Herausgegeben:de Bruijne, Marleen; Cattin, Philippe C.; Cotin, Stéphane; Padoy, Nicolas; Speidel, Stefanie; Zheng, Yefeng; Essert, Caroline

• Broschiertes Buch

Produktbeschreibung

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 531 revised full papers presented were carefully reviewed and selected from 1630 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 - attention models; 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.

Produktdetails

• Lecture Notes in Computer Science 12907
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-87233-5
• 1st ed. 2021
• Seitenzahl: 844
• Erscheinungstermin: 24. September 2021
• Englisch
• Abmessung: 235mm x 155mm x 45mm
• Gewicht: 1253g
• ISBN-13: 9783030872335
• ISBN-10: 3030872335
• Artikelnr.: 62416237

Inhaltsangabe

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 viaMulti-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 inChildren.- 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 Imagesfrom 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.