Greenspan / Madabhushi / Mousavi Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part I
Erscheinungsjahr 2023
ISBN: 978-3-031-43907-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 785 Seiten

Reihe: Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Greenspan, Hayit

Madabhushi, Anant

Mousavi, Parvin

Salcudean, Septimiu

Duncan, James

Syeda-Mahmood, Tanveer

Taylor, Russell

Inhaltsverzeichnis

Machine learning with limited supervision.- machine learning – transfer learning.