About

In recent years, machine learning has sparked significant research interest, particularly in the realm of biological and medical imaging. Numerous deep learning-based methods and tools have been crafted to enhance the analysis and interpretation of biomedical images. As the scale and complexity of these images grow rapidly, the field is undergoing an intelligent evolution, presenting new challenges that await solutions. One of the primary challenges is developing deep learning techniques that fully leverage their ability to discern patterns and relationships within image data. This workshop will spotlight the latest advancements in machine learning techniques tailored for biological and medical imaging and their subsequent analysis. We aim to provide a collaborative platform for experts and scholars worldwide to exchange ideas and share progress. Join us in navigating the future of machine learning in biomedical imaging, as we tackle these challenges and push the boundaries of innovation.

Topics of interest include, but are not limited to:

1. Deep Learning Techniques for Image Analysis
2. Big Data Challenges in Biomedical Imaging
3. Image Segmentation and Classification
4. Applications of Convolutional Neural Networks in Medical Imaging
5. Data Augmentation and Synthesis for Medical Images
6. Explainability and Interpretability of Machine Learning Models

Important dates:

• Electronic submission of full papers: October 18, 2024
• Notification of paper acceptance: October 25, 2024
• Camera-ready of accepted papers: November 10, 2024
• Workshop: Lisbon, Portugal, December 3-6, 2024

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