## 1 Introduction  
The tremendous development of technology also affects medical science, including imaging diagnostics. Computer tomographs enable the non-invasive visualization of internal human organs and tissues without the need for a surgical operation. This leads to the research and development of new, more efficient, and reliable diagnostic and therapeutic procedures. Medical imaging, such as biomedical signal acquisition, plays an increasingly important role not only in diagnostics, but also in therapy, monitoring its effects, and in rehabilitation. On the other hand, the increasing data sets generated by medical diagnostic devices make it difficult to explore and analyze data in a non-automatic way--as doctors have done so far. Hence, recently, more and more attention has been paid to the design and development of automatic data analysis systems, the application of which covers various issues in the field of healthcare. Such systems can dynamically adapt to changing conditions, and thus facilitate the analysis and solving of complex problems. Such systems often implement machine learning methods. Machine learning (ML) is a subset of artificial intelligence (AI). Algorithms are trained to find patterns and correlations in large data sets, and to make the best decisions as well as predictions based on the results of such analysis. Machine learning systems become more effective over time, and the more data they have access to, the more accurate they are. Nowadays, deep learning methods are also often used in medical imaging [1]. Deep learning is a part of machine learning. It is based on complex artificial neural networks. The learning process is deep because the structure of artificial neural networks consists of many input, output, and hidden layers, which are often interconnected. Deep networks achieve much better results in terms of the recognition, classification, and prediction of medical data compared to classical machine learning algorithms. Thanks to ML technology, including DL, health care workers, including doctors, can cope with complex problems that would be difficult, time-consuming, and ineffective to solve on their own. This Special Issue includes 10 publications that discuss the use of broadly understood machine learning methods for processing and analyzing biomedical signals and images coming from many medical modalities. The use of these methods allows a better understanding of the principles of the human body functioning at various levels (cellular, anatomical, and physiological) by providing additional, quantitative, reliable data extracted from medical data. [PERSON] [2] proposes an algorithm developed for automated electrocardiogram (ECG) classification. ECG is a popular biosignal in heart disease diagnostics. However, it is non-stationary; thus, the implementation of classic signal analysis techniques (such as time-based analysis feature extraction and classification) is rather difficult. Thus, a machine learning approach based on the ensemble of depthwise separable convolutional (DSC) neural networks for the classification of cardiac arrhythmia ECG beats was proposed. This method reduces the standard path of ECG analysis (QRS detection, preprocessing, feature extraction, and classification) to two steps only, i.e., QRS detection and classification. Since feature extraction was combined with classification, no ECG preprocessing was required. To reduce the computational cost and maintain method reliability, All Convolutional Network(ACN), Batch Normalization (BN), and ensemble convolutional neural networks were implemented. The developed ensemble of deep networks was validated using the MIT-BIH arrhythmia database. The obtained classification results (16 class problem) resulted in sensitivity (Sn), specificity (Sp), and positive predictivity (Pp), and accuracy (Acc) equal to 99.03%, 99.94%, 99.03%, and 99.88%, respectively. It was demonstrated that presented classification quality measures outperformed other state-of-the-art methods.