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

… Our results show that deep learning methods are able to learn physiologically important …
Our goal is to validate feasibility of this application by (a) investigating if a building block of deep …

… deep learning applications has risen tremendously over the past years. Deep learning has
… misconducts when training and validating deep learning systems are discussed and some …

… recent advances have applied deep learning to prostate cancer … prior studies by applying
deep learning to conduct Gleason … To this end, we developed a deep learning system (DLS) to …

… engineering by learning the … deep learning 7 ,8 was used to train an algorithm to detect
referable diabetic retinopathy and assess the performance of the algorithm in 2 clinical validation …

… Development and validation of a deep learning-based automated detection … validation
is lacking in this study. Further study is needed to validate the advantages of deep learning …

… To develop a deep learning–based algorithm that can classify normal and abnormal results
… and pneumothorax and to validate the algorithm’s performance using independent data sets. …

… Our deep learning model can … that deep learning models can improve the performance of
clinical experts during medical imaging interpretation. Further research is needed to validate …

… Early work in automatic polyp detection has focused on applying deep-learning techniques
… validation sets 19,20 . Here, we report the development and validation of a deep-learning …

… Deep learning is a method premised on learning complex hierarchical representation from
the data that constitute multiple levels of abstraction. Deep learning … of deep learning have …