Minimizing bias is critical to adoption and implementation of machine learning (ML) in
clinical practice. Systematic mathematical biases produce consistent and reproducible …

In the last decade, the focus of computational pathology research community has shifted
from replicating the pathological examination for diagnosis done by pathologists to …

Background Breast cancer is highly heterogeneous, resulting in different treatment
responses to neoadjuvant chemotherapy (NAC) among patients. A noninvasive quantitative …

Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics
offer great promise for clinical use. However, many of these methods have limited clinical …

The recent advancements in artificial intelligence (AI) combined with the extensive amount
of data generated by today's clinical systems, has led to the development of imaging AI …

Abstract Machine learning has been increasingly applied to neuroimaging data to predict
age, deriving a personalized biomarker with potential clinical applications. The scientific and …

Medical imaging is a great asset for modern medicine, since it allows physicians to spatially
interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and …

Deep learning has become a leading subset of machine learning and has been successfully
employed in diverse areas, ranging from natural language processing to medical image …

Abstract Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder
characterized by motor neuron degeneration. Significant research has begun to establish …

Many studies on machine learning (ML) for computer-aided diagnosis have so far been
mostly restricted to high-quality research data. Clinical data warehouses, gathering routine …