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This systematic review and meta-analysis statistically appraises pooled data collected from 279 studies. It is the largest study to date examining the diagnostic accuracy of DL on medical...
Materials and Methods: In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for im-age-based radiologic diagnosis that included external validation, published from Jan...
The computer processing and analysis of medical images involve image retrieval, image creation, image analysis, and image-based visualization [2]. Medical image processing had grown to include com-puter vision, patter...
In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in v...
Applying these standards meant that only appropriate, high-quality studies were included in the review and provided a strong foundation upon which to evaluate the advancements in deep learning for medical imaging, as ...
Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 w...
We focused on open science medical imaging research, the currently available open imaging data sets for deep learning, and also on the open-source software packages that are available for medical image processing.
By synthesizing the latest advancements and analyzing contemporary challenges, this paper presents guidelines for future studies that are vital for the development of robust, ethical, and clinically effective deep lea...
The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed in this review from a practical standpoint of a deep learning researcher designing a system for the cl...
Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images
