Yuyin Zhou – författare

This book constitutes the refereed proceedings of the 1st International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis, MOVI 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, Singapore, in September 2022.

The 18 papers presented at MOVI 2022 were carefully reviewed and selected from 25 submissions. The objective of the MOVI workshop is to promote novel scalable and resource-efficient medical image analysis algorithms for high-dimensional image data analy-sis, from optical imaging to virtual microscopy.

This book constitutes the proceedings of Second International Workshop on Trustworthy Artificial Intelligence for Healthcare, TAI4H 2024, held in Jeju, South Korea, in August 2024, in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI 2024. 

The 13 full papers included in this book were carefully reviewed and selected from 21 submissions. They focus on trustworthy artificial intelligence, healthcare, generalization, explainability, fairness, privacy, multi-modal fusion, foundation models.

This book constitutes the refereed proceedings of the Second International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis, MOVI 2024, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, in October 2024.

The 21 regular papers presented at MOVI 2024 were carefully reviewed and selected from 29 submissions.

They are grouped into these two topical sections: Medical Optical Imaging and Virtual Microscopy Image Analysis and Kidney Pathology Image segmentation (KPIs) Challenge.

This LNCS conference set constitutes the proceedings of the First Medical Image Segmentation Challenge, MedSAM on Laptop 2024, Held in Conjunction with CVPR 2024, in Seattle, WA, USA, held in June 2024.

The 16 full papers presented were thoroughly reviewed and selected from the 200 submissions. This challenge aims to prompt the development of universal promotable medical image segmentation foundation models that are deployable on laptops or other edge devices without reliance on GPUs.