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Deep Learning: Ai and Healthcare (overview) Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ AI and Healthcare (overview)

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  • “In conclusion, with the current fast pace in development of machine learning techniques, and deep learning in particular, there is prospect for a more widespread clinical adoption of machine learning in radiology practice. Machine learning and artificial intelligence are not expected to replace the radiologists in the foreseeable future. ese techniques can potentially facilitate radiology work ow, increase radiologist productivity, improve detection and interpretation of findings, reduce the chance of error, and enhance patient care and satisfaction.”


    Current Applications and Future Impact of Machine Learning in Radiology 
Garry Choy et al.
 Radiology 2018; 00:1–11
  • “Radiomics is a process designed to extract a large number of quantitative features from radiology images . Radiomics is an emerging field for machine learning that allows for conversion of radiologic images into mineable high-dimensional data. For instance, Zhang et al evaluated over 970 radiomics features extracted from MR images by using machine learning methods and correlated with features to predict local and distant treatment failure of advanced nasopharyngeal carcinoma.”


    Current Applications and Future Impact of Machine Learning in Radiology 
Garry Choy et al.
 Radiology 2018; 00:1–11
  • “Machine learning approaches to the interrogation of a wide spectrum of such data (sociodemographic, imaging, clinical, laboratory, and genetic) has the potential to further personalize health care, far beyond what would be possible through imaging applications alone. Precision medicine require the use of novel computational techniques to harness the vast amounts of data required to discover individualized disease factors and treatment decisions.”


    Current Applications and Future Impact of Machine Learning in Radiology 
Garry Choy et al.
 Radiology 2018; 00:1–11
  • AI in Healthcare

  • AI in Healthcare

  • “Deep learning is a type of representation learning in which the algorithm learns a composition of features that re ect a hierarchy of structures in the data. Complex representations are expressed in terms of simpler representations.”
Deep Learning: A Primer for Radiologists.”

    
Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “Although neural networks have been used for decades, in re- cent years three key factors have enabled the training of large neural networks: (a) the availability of large quantities of la- beled data, (b) inexpensive and powerful parallel computing hardware, and (c) improvements in training techniques and architectures.”
Deep Learning: A Primer for Radiologists.”


    Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “Deep CNNs exploit the compositional structure of natural images so that shifts and deformations of objects in the images do not significantly affect the overall performance of the network.”
Deep Learning: A Primer for Radiologists.”


    Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “The creation of these large databases of labeled medical images and many associated challenges will be fundamental to foster future research in deep learning applied to medical images.”
Deep Learning: A Primer for Radiologists.”


    Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
© 1999-2018 Elliot K. Fishman, MD, FACR. All rights reserved.