Medical Image Computing and Computer Assisted Intervention – MICCAI 2023〈1st ed. 2023〉 : 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part II

著者名：Greenspan, Hayit (EDT)/Madabhushi, Anant (EDT)/Mousavi, Parvin (EDT)/Salcudean, Septimiu (EDT)/Duncan, James (EDT)/Syeda-Mahmood, Tanveer (EDT)

Springer（2023/09/30発売）

• 言語：ENG
• ISBN：9783031438943
• eISBN：9783031438950

Description

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.

Table of Contents

Machine learning – learning strategies.- machine learning – explainability, bias, and uncertainty.