Dear colleagues,

The section “Machine and Deep Learning Technologies in Imbalanced Medical Imaging” will contain an extensive investigation into the complex interactions that exist between cutting-edge computing technologies and the enduring difficulties related to imbalanced datasets in the field of medical imaging. Developing reliable and effective machine learning and deep learning models for medical image analysis is severely hampered by data distribution imbalances [1], which result in the underrepresentation of particular medical disorders or classes [2,3]. This section will serve as a comprehensive repository for novel methodologies, strategies, and innovations dedicated to addressing these issues. Researchers and practitioners are invitied to contribute valuable insights into the development of intelligent algorithms, innovative neural network architectures, and sophisticated data preprocessing techniques [4,5]. The emphasis will be given on advancing the field beyond the limitations imposed by imbalanced datasets [6], ensuring that these technologies can be effectively applied to improve diagnostic accuracy and analytical processes in healthcare. The collaboration between machine learning, deep learning, and medical imaging is explored in depth, with a focus on real-world applications. By showcasing successful approaches and novel techniques, the section aims to facilitate a deeper understanding of the challenges associated with imbalanced datasets in medical imaging and provide practical solutions.

This curated collection will act as a collaborative platform, promoting the exchange of ideas and methodologies among researchers and practitioners. By promoting interdisciplinary collaboration, the section aims to accelerate progress in overcoming the challenges posed by imbalanced medical imaging datasets. The ultimate oal is to harness the full potential of machine and deep learning technologies, providing clinicians with powerful tools for improved medical diagnostics and patient care.