Biosignal and Medical Image Processing

Dear Colleagues,

The success of recent machine learning approaches in a variety of research areas has pushed automated data analysis towards human (if not superhuman) performance in several tasks. However, some domain-specific problems may present certain difficulties that current deep learning approaches are not able to fully overcome.

Among these problems, medical data analysis is probably the one for which most interest and efforts are dedicated, especially in these times of health crisis due to the COVID-19 pandemic. Indeed, while it is possible to apply learning techniques that work well on general-purpose applications as well as with medical data, medical data analysis is particularly complex, for several reasons.

First and foremost, the lack of large-scale annotated datasets, which has proven to be a key factor for domain-generic tasks, is a main hindrance to the development of tools that can successfully and reliably support physicians in their jobs. As an additional difficulty, dataset fusion is made more complex by the huge variability introduced by measuring/scanning devices, environmental conditions of exams, and population-specific factors, making generalizable models hard to train.

Moreover, while a lot of research on medical data analysis has focused on medical image analysis, which is naturally supported by the vast literature on computer vision, there are several data modalities that can be equally important but have not attracted as much interest by scientists, e.g., EEG and ECG.

Finally, while many existing techniques target the problem of disease diagnosis using supervised approaches, few works tackle the problem of knowledge discovery from medical data, in order to understand more about the way certain physiological processes work—the inner working of our brain is the most evident example.

This Special Issue has the objective of collecting novel and impactful works on biosignal and medical image processing, including but not limited to the following topics:

• Medical image analysis;
• Medical video analysis;
• Medical biosignal analysis;
• Dataset collection for medicine;
• Medical dataset fusion and integration;
• Multi-modal approaches for medicine;
• Transfer learning approaches for medicine;
• Continual learning in medicine;
• Diagnosis approaches for known diseases;
• Unsupervised approaches for knowledge discovery in medicine.

Dr. Simone Palazzo
Dr. Carmelo Pino
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.