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

by Linwei Wang (Editor), Qi Dou (Editor), P. Thomas Fletcher (Editor), Stefanie Speidel (Editor), Shuo Li (Editor)

Overview

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.

Product Details

ISBN-13:	9783031164514
Publisher:	Springer Nature Switzerland
Publication date:	09/16/2022
Series:	Lecture Notes in Computer Science , #13438
Edition description:	1st ed. 2022
Pages:	740
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Machine learning – weakly-supervised learning.- machine learning – model interpretation.- machine learning – uncertainty.- machine learning theory and methodologies.