Machine Learning for Classification and Analysis of Biomedical Images

Dear Colleagues,

In recent decades, thanks to the increasing performance of GPUs, there have been many advances in the field of instrumentation for the acquisition of medical images and in the field of computing capacity. This has given rise to software tools, mainly based on artificial intelligence, dedicated to the analysis and classification of increasingly refined and potentially informative biomedical images. With this Special Issue, we intend to promote the sharing in the international scientific community of the most recent and in-depth research on the use of biomedical image analysis (preprocessing, harmonization, segmentation, and pattern recognition) and classification methods based on machine learning and deep learning. Attention will be dedicated, but not limited to, diagnostic methods, such as X-ray, mammography, DBT, CT, MRI, PET, SPECT, ARUS, optical imaging, CLI, photoacoustic imaging, and biopsy samples, with focus, when applicable, on disease detection and classification but also on AI (artificial intelligence)-enhanced low-dose image reconstruction. The images linked to radiotherapy treatments, monitoring, and follow up will also be considered.

Given the rising importance of appropriately sized datasets for ML/DL (machine learning/deep learning) training in the biomedical field, related topics will also be considered, such as training/test strategies, reproducibility of ML/DL findings, and harmonization strategies for heterogeneous datasets.

Authors are encouraged to submit contributions in any of the following or related research topics:

Classification of biomedical images;

Segmentation of biomedical images by means of ML/DL systems;

Computer-aided detection;

Computer-aided diagnosis;

ML/DL techniques for image processing;

Image reconstruction by means of ML/DL systems;

Preprocessing of biomedical images aimed at ML/DL analysis;

Approaches for heterogeneous datasets for ML/DL training;

Training/test strategies for ML/DL applications in biomedical imaging;

Explainability of DL methods in biomedical imaging.

Prof. Dr. Maria Evelina Fantacci
Prof. Dr. Piernicola Oliva
Guest Editors

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• deep learning
• machine learning
• biomedical image processing
• computer-aided detection
• computer-aided diagnosis