The advancement and application of deep learning techniques in the field of biomedical image analysis have experienced significant growth, driven by the ever-increasing sophistication of computational models and the availability of extensive imaging datasets. This dissertation presents an exploration into how deep learning can be leveraged to enhance diagnostic accuracy, risk stratification, and biomarker identification in various clinical contexts. Through a series of studies, we demonstrate the potential of deep learning frameworks to not only improve the classification of medical conditions—such as fatty liver disease and metabolic syndrome from abdominal imaging—but also to predict future disease risks, thereby facilitating early intervention strategies. Additionally, we show how integrating multiple learning strategies can improve biomarker prediction from histology whole slide images.In these investigations, deep learning models were trained to interpret complex imaging data, enabling the identification of subtle, often imperceptible patterns associated with pathological changes. The results underline the power of these models to surpass traditional imaging analysis techniques in both efficacy and efficiency. The findings underscore the transformative potential of deep learning in medical imaging, suggesting a shift towards more predictive, personalized healthcare. The integration of deep learning models into clinical practice promises not only to enhance diagnostic and prognostic capabilities but also to pave the way for advancements in precision medicine. Future directions are discussed, emphasizing the need for prospective longitudinal studies, integration into clinical workflows, and the increasing power of foundation models in the computational analysis of biomedical imaging.