# Machine Learning Algorithms for Biomedical Image Analysis and Their Applications
###### Abstract  
In recent years, architectural and algorithmic innovations in machine learning have revolutionized the analysis of medical images. Despite these advances, integrating these models into clinical practice comes with several challenges. The wide availability of data and their heterogeneity offer opportunities for us to train increasingly ambitious models. However, several challenges still need to be addressed: the need for multimodal training, data harmonization, the training of small dataset scenarios, etc. [1]. In addition, explainability and reliability requirements imposed by regulatory agencies add further complexity to the integration of machine learning models into clinical settings. As a consequence, it is essential to address these challenges to advance precision and personalized medicine. This Special Issue contains published articles on significant advancements in the application of artificial intelligence (AI) and machine learning (ML). Several image modalities, including magnetic resonance imaging (MRI), computed tomography (CT), electrocardiography (ECG), positron emission tomography-computed tomography (PET-CT), and histopathology were included in these articles. Motion correction, fracture detection, disease classification, radiomics, and predictive modelling were the tasks investigated, emphasizing the potential for AI-driven solutions in medical imaging tasks. Radiomics is an innovative framework for medical image analysis [2]. Thousands of features can be extracted of imaging patterns associated with clinical features or disease outcomes [3]. [PERSON] et al. [4] developed and validated a machine learning model that uses radiomic features extracted from PET-CT scans to predict the future growth rate of abdominal aortic aneurysms. Their study tested several machine learning models using the radiomic features mentioned above, obtaining promising results. However, the ability of deep architectures to extract high- and low-level features makes deep-feature models much more informative than radiomic ones. From this perspective, [PERSON] et al. [5] selected three different deep architectures, fine-tuned them, and used them as base models, combined with a Bayesian-based probabilistic ensemble learning method, for fracture detection in cervical spine CT images. The proposed method considers the prediction uncertainty of the base models and combines the predictions obtained from each of them to improve its overall performance significantly. Heart disease is the leading cause of death worldwide, making the early, accurate, and effective diagnosis of heart disease crucial to saving lives. However, making manual interpretations of ECG imaging--a primary non-invasive method used to identify cardiac abnormalities--for heart disease diagnosis is a time-consuming and inaccurate process. In [PERSON] et al. 's study [6], a hybrid residual/inception-based deeper model is proposed for the accurate and efficient detection of heart diseases from ECG recordings. The result of this study indicates that theproposed model is a potential new method for predicting heart diseases using ECGs. The movement of human subjects during medical image acquisition is unavoidable and can lead to motion-induced artifacts, which can deteriorate the quality of images and reduce diagnostic accuracy. Deep learning-based motion correction methods could provide a solution to this. The aim of the study proposed by [PERSON] et al. [7] was to develop and evaluate a deep learning-based method for suppressing motion artifacts in brain magnetic resonance imaging (MRI) scans. The proposed approach is able to effectively suppress motion artifacts in brain MRI scans without compromising image quality. Given the efficiency of MC-Net (which has a single-image processing time of 40 ms), it is potentially usable in clinical settings. Models based on deep architectures, particularly convolutional neural networks (CNNs), have shown a remarkable ability to classify pathological images.