Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …

Although advances in deep learning systems for image-based medical diagnosis
demonstrate their potential to augment clinical decision-making, the effectiveness of …

Despite continued advancement in recent years, deep neural networks still rely on large
amounts of training data to avoid overfitting. However, labeled training data for real-world …

The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a
wave of opportunities in computational healthcare. The interactive nature of these models …

Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

While hundreds of artificial intelligence (AI) algorithms are now approved or cleared by the
US Food and Drugs Administration (FDA), many studies have shown inconsistent …

Recent advances in synthetic imaging open up opportunities for obtaining additional data in
the field of surgical imaging. This data can provide reliable supplements supporting surgical …

Neuroimaging of large populations is valuable to identify factors that promote or resist brain
disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such as …

Artificial Intelligence (AI) has proven that can be a precious tool in the healthcare domain,
via the automation of menial tasks and the assistance provided to healthcare providers and …