Publications

Membership has benefits


The MICCAI Society has partnered with Elsevier, an international publisher of both journals and books, to develop a book series that compiles the latest research, methodologies, and innovations in the field of medical image computing (MIC) and computer-assisted interventions (CAI).

Publications in the MICCAI Book Series typically include papers from the MICCAI conference, covering topics such as image processing, machine learning in healthcare, medical robotics, diagnostic tools, and computer-aided surgery, among others. The series serves as an important resource for university and industry researchers, undergraduate and post graduate students, and practitioners involved in the fields of medical imaging and healthcare technology.

MICCAI members in good standing receive a 30% discount when they order online and enter the code ENGIN30.

 

New in the MICCAI Book Series 

Trustworthy AI in Medical ImagingTrustworthy AI in Medical Imaging
Marco Lorenzi and Maria A. Zuluaga

Trustworthy AI in Medical Imaging brings together scientific researchers, medical experts, and industry partners working in the field of trustworthiness, bridging the gap between AI research and concrete medical applications and making it a learning resource for undergraduates, masters students, and researchers in AI for medical imaging applications. 

Federated Learning for Medical ImagingFederated Learning for Medical Imaging
Xiaxiao Li, Ziyue Xu and Huazhu Fu

Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging.