Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part VII

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.

List of contents

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 inC

Product details

• Assisted by: Philipp C Cattin (Editor), Philippe C Cattin (Editor), Philippe C. Cattin (Editor), Stéphane Cotin (Editor), Stéphane Cotin et al (Editor), Marleen de Bruijne (Editor), Caroline Essert (Editor), Nicolas Padoy (Editor), Stefanie Speidel (Editor), Yefeng Zheng (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 22.10.2021
• EAN: 9783030872335
• ISBN: 978-3-0-3087233-5
• No. of pages: 801
• Dimensions: 155 mm x 44 mm x 235 mm
• Illustrations: XXXIX, 801 p. 277 illus., 258 illus. in color.
• Series: Lecture Notes in Computer Science; Image Processing, Computer Vision, Pattern Recognition, and Graphics
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > Application software