Biomedical images and signals (e.g., magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), whole slide images (WSIs), electrocardiogram (ECG), electroencephalogram (EEG), electromyography (EMG), etc.) are very useful for evaluating the well-being of a human. To diagnose the abnormality in a certain part of the body or a particular organ, doctors use these images, signals, and clinical documents as a particular media. Over the last few decades, the progress in image and signal processing has enabled automatic analysis by using excellent resolution and quality datasets.

However, most of the state-of-the-art methods still fail to convey the actual scenario of the body part or organ. Modelling and machine learning methods play important roles in dealing with biomedical signals or images. Their applications include noise reduction, artifact removal, and early detection of cancer. Moreover, these methods can be used for tumour analysis, a fusion of multi-module data for better diagnosis, classification of signals/images, etc.

The aim of this Special Issue is to bring together original research and review articles discussing novel research outcomes of various modelling and machine learning applications. Submissions can include biomedical signals, images, and other types of biomedical or clinical data.

Potential topics include but are not limited to the following:

• Biomedical signal analysis using machine learning and modelling
• Machine learning approaches in medical image analysis and processing
• Machine learning in computational systems
• Modelling and simulation methods in medicine using machine learning
• Machine learning and multiscale models for synthetic biology
• Cardiovascular and respiratory systems engineering using machine learning and modelling
• Therapeutic diagnostic systems and technologies using machine learning and modelling
• Recommender systems using machine learning and modelling