Dear Colleagues,

Since the beginning of the 21st century, the connectome has been used in various medical imaging tasks and electrophysiological applications, propelling research into the era of artificial intelligence. This Special Issue will highlight both clinical needs and technical challenges in medical imaging processing and describe how emerging trends in connectome are addressing these issues. Topics covered will include network architecture, learning, interpretability, uncertainty quantification. We are looking to present studies that are related to digital pathology mainly in the brain, particularly prominent research highlights related to clinical study applications. Artificial intelligence and neural networks are significant procedures that tackle this kind of medical diagnosis issue. Moreover, medical imaging processing presents a unique challenge that confronts other connected approaches such as deep learning and different machine learning techniques, which are employed in a variety of tasks along with the different medical image modalities. Disease prediction is one of the basic tasks for medical diagnosis software. As of late, deep learning strategies have been effectively used in different applications to aid medical diagnosis. The contribution of deep learning traits and learning its concepts is helpful for the medical community. We will address research challenges and suggested solutions, as well as future promises and directions for further developments. Proposing new neural network algorithms, different machine learning techniques, and deep learning architectures could improve the detection of brain anomalies, providing timely diagnosis that could reduce the patient's risk for long-term symptoms and lasting deficits. This Special Issue will show the importance of these techniques in medical diagnostics and present future research topics that could be utilized to lead further research. 

Dr. Juliana Dushanova
Guest Editor

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