Deep Learning Fundamentals

In this new generation of artificial intelligence (AI), data-driven and end-to-end paradigms have been established. These paradigms have led to substantial breakthroughs in data analysis, particularly in the context of medical imaging. These advancements have given rise to new study topics in the field of precision medicine, accompanied by novel applications in medical imaging that have never been seen before. The popularity of deep learning (DL) as a data science discipline has skyrocketed in recent years. In particular, in imaging competitions such as ImageNet, the first DL models demonstrated exceptional performance, outclassing the traditional machine learning models by a wide margin. Nevertheless, the origins of DL can be traced back several years earlier. The human nervous system served as the inspiration for artificial neural networks (ANN), which were initially developed with the goal of deciphering the biological functions of the brain. ANNs are still the foundation of many deep learning architectures, such as convolutional neural networks and visual transformers. It is well-known that modern deep learning-based decision systems need massive datasets to train models that are both generalizable and trustworthy. However, gathering these large datasets is not a trivial task since it necessitates time-consuming methods involving not just medical professionals but also experienced data scientists, both of whom have limited time. Furthermore, collecting data is dependent on elaborate and expensive processes that might give rise to ethical and legal concerns. When compared to the natural imaging field, medical imaging presents unique difficulties in terms of data collection due to the wide variety of lesion types, anatomical regions, imaging modalities, scanner types, and lack of imaging standards among vendors. Moreover, the lack of large patient cohorts with meaningful clinical data on targeted endpoints for addressing unmet clinical needs is the greatest disadvantage of developing DL in medical imaging applications. Transfer learning has been attracting a significant amount of research attention in the development of robust models with size-limited data distribution. DL-based models have proven transferable to a number of medical imaging tasks, advancing lesion detection and diagnosis. Generic examples of samples can be created from noise with deep generative models, like generative adversarial networks and variational autoencoders, by learning the underlying distribution of the desired set of data. By synthetically increasing the patient cohort with a robust and diverse distribution, deep generative models have the ability to mitigate the limited resources during the data collection phase of an AI project. Federated learning and the rise of cloud computing have the potential to tap into private datasets that are not accessible due to privacy concerns, data ownership, and the sensitive nature of health information. The higher the sophistication of a DL model, the less clear and easy it may be to interpret as a result of the large number of hidden parameters and complex processes. The explanation of the analysis steps that the AI model follows is interwoven with trust, clear or understandable processes, and accountability for the decision-making process. The explainability of a DL-powered computer-aided system is dependent on a set of parameters that reflect every phase of its lifetime cycle and is key to its adaptation in the medical field by both patients and medical professionals. In this book chapter, a brief introduction of the key concepts, terminology, and architectures of DL is presented in the section "Deep Learning in Medical Imaging," including the novel deep generative models and visual transformers. The sections "Quality and Biases of Medical Databases" and "Pre-processing for Deep Learning" will shed light on topics such as the challenges of medical imaging data collection, handling, and the commonly used pre-processing methods, which are highly neglected parts in most AI papers. Insights about different training techniques such as transfer, ensemble, federated, and other learning types will be discussed in the section "Learning Strategies." Finally, in the section "Interpretability and Trustworthiness of Artificial Intelligence" the importance of traceability, reproducibility, and explainability of AI models is highlighted.