Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 : 25th International Conference, Singapore, September 18-22, 2022, Proceeding

Wang, Linwei

ISBN 10: 3031164482 / ISBN 13: 9783031164484

Published by Springer 2022-09, 2022

About this Item

Seller Inventory # 6666-IUK-9783031164484

Bibliographic Details

• Title: Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 : 25th International Conference, Singapore, September 18-22, 2022, Proceeding
• Publisher: Springer 2022-09
• Publication Date: 2022
• Language: English
• ISBN 10: 3031164482
• ISBN 13: 9783031164484
• Binding: PF
• Condition: New

About this title

Synopsis

The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.

The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:

Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;

Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;

Part III: Breast imaging; colonoscopy; computer aided diagnosis;

Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;

Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;

Part VI: Image registration; image reconstruction;

Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization;

Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.