Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …

Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …

Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …

Deep learning, for image data processing, has been widely used to solve a variety of
problems related to medical practices. However, researchers are constantly struggling to …

One of the most common and deadly diseases in the world is lung cancer. Only early
identification of lung cancer can increase a patient's probability of survival. A frequently used …

Deep learning methods have huge success in task specific feature representation. Transfer
learning algorithms are very much effective when large training data is scarce. It has been …

Simple Summary Lung cancer screening has been shown to help reduce mortality in
selected populations of smokers; however, performing screening programs at a larger scale …

Breast cancer prognosis is steadily improving due to early detection of primary cancer in
screening programs and revolutionizing treatment development. In the metastatic setting …

Rationale and Objectives Histological subtypes of lung cancers are critical for clinical
treatment decision. In this study, we attempt to use 3D deep learning and radiomics methods …

Due to the lack of properly annotated medical data, exploring the generalization capability of
the deep model is becoming a public concern. Zero-shot learning (ZSL) has emerged in …