Machine Learning Algorithms for Biomedical Image Analysis and Their Applications

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. 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.