Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

24th International Conference, Strasbourg, France, September 27 – October 1, 2021, Proceedings, Part VII

Häftad, Engelska, 2021

AvMarleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert,Stephane Cotin

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and machine learning - weakly supervised learningPart III: machine learning - advances in machine learning theory; and clinical applications - vascularPart VII: clinical applications - abdomen; and clinical applications - oncologyPart VIII: clinical applications - ophthalmology;

Produktinformation

Utgivningsdatum2021-09-24
Mått155 x 235 x 45 mm
Vikt1 253 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor801
FörlagSpringer Nature Switzerland AG
ISBN9783030872335

Tillhör följande kategorier

Tillämpad datateknik inom Data och it
Systemvetenskap och AI inom Data och it
Biovetenskap inom Naturvetenskap och teknik
Medicinsk bildbehandling inom Medicin
Artificiell intelligens inom Data och it

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.