About

Over the past few years, machine learning has generated overwhelming research interests. Especially in the field of biological and medical imaging, numerous deep learning-based methods and tools have been developed to analyze biomedical images and to improve biomedical image interpretations. With the rapid increase of the scale and the size of such images, biological and medical image processing is now facing an intelligent evolution. However, there are still many challenges waiting to be addressed. For example, one major challenge is to develop techniques through deep learning models by taking advantage of their great abilities to learn patterns and relationships in image data. This workshop will focus on the latest development of machine learning-based biological and medical imaging techniques and the subsequent analysis. It will provide a platform for experts and scholars around the world to communicate and share progresses.

Topics of interest include, but are not limited to:

1. Machine learning methods in biological imaging, such as Electron Microscopy, Fluorescence Imaging, and Mass-Spectrometry Peptide sequencing.
2. Machine learning methods in medical image processing, such as disease diagnosis with MRI and CT images.
3. Intelligent applications of biological and medical big data including biological and medical images.
4. Intelligent analysis and visualization of biological and medical images.
5. Construction of biological and medical big data including biological and medical images.

Important dates:

• Electronic submission of full papers: October 17, 2023
• Notification of paper acceptance: October 31, 2023
• Camera-ready of accepted papers: November 21, 2023
• Workshop: Istanbul, Turkey, December 5-8, 2023

Program Co-chairs:

• Dr. Fa Zhang, Professor, Beijing Institute of Technology, China
• Dr. Xuefeng Cui, Professor, Shandong University, China

Program Committee Members:

• Dr. Hongmin Cai, Professor, South China University of Technology, China
• Dr. Xin Gao, Professor, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
• Dr. Xiaowen Liu, Professor, Tulane University, USA
• Dr. Xiaohua Wan, Professor, Beijing Institute of Technology, China
• Dr. Lusheng Wang, Professor, City University of Hong Kong, China
• Dr. Lei Xie, Professor, City University of New York, USA
• Dr. Luoxin Zhang, Professor, National University of Singapore, Singapore
• Dr. Daming Zhu, Professor, Shandong University, China