Medical Image Computing and Computer Assisted Intervention - MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part III

by Hayit Greenspan (Editor), Anant Madabhushi (Editor), Parvin Mousavi (Editor), Septimiu Salcudean (Editor), James Duncan (Editor)

Overview

The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023.

The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections:

Part I: Machine learning with limited supervision and machine learning – transfer learning;

Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty;

Part III: Machine learning – explainability, bias and uncertainty; image segmentation;

Part IV: Image segmentation;

Part V: Computer-aided diagnosis;

Part VI: Computer-aided diagnosis; computational pathology;

Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular;

Part VIII: Clinical applications – neuroimaging; microscopy;

Part IX: Image-guided intervention, surgical planning, and data science;

Part X: Image reconstruction and image registration.

Product Details

ISBN-13:	9783031438981
Publisher:	Springer-Verlag New York, LLC
Publication date:	09/30/2023
Series:	Lecture Notes in Computer Science , #14222
Sold by:	Barnes & Noble
Format:	eBook
File size:	127 MB
Note:	This product may take a few minutes to download.

Table of Contents

Machine learning – explainability, bias and uncertainty.- image segmentation.