TY - GEN
T1 - Deep Learning for Automating the Organization of Institutional Dermatology Image Stores
AU - Wang, Michael Z.
AU - Comfere, Nneka I.
AU - Murphree, Dennis H.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - A common challenge faced by researchers associated with healthcare institutions is that data of interest are often contained in electronic medical informatics systems that are centered on optimizing clinician/clinician and patient/clinician communication. While this focus naturally enhances the primary goal of care delivery, it is often suboptimal for secondary research purposes. For example at our institution while it is easy for a clinician to view images associated with a specific patient visit, it remains a challenge for an investigator to assemble a cohort of specific images in order to further research objectives. In order to address this important optimization gap we have developed a system for automated image categorization based on a deep neural network. This image classifier organizes the contents of an electronic health record system in a manner which is more amenable to further research by specifically dividing all available images into a lexicon of subclasses. While the current study is focused on dermatology-related images collected by a combined primary and tertiary care center, we expect similar approaches to aid a variety of institutions and clinical specialties.
AB - A common challenge faced by researchers associated with healthcare institutions is that data of interest are often contained in electronic medical informatics systems that are centered on optimizing clinician/clinician and patient/clinician communication. While this focus naturally enhances the primary goal of care delivery, it is often suboptimal for secondary research purposes. For example at our institution while it is easy for a clinician to view images associated with a specific patient visit, it remains a challenge for an investigator to assemble a cohort of specific images in order to further research objectives. In order to address this important optimization gap we have developed a system for automated image categorization based on a deep neural network. This image classifier organizes the contents of an electronic health record system in a manner which is more amenable to further research by specifically dividing all available images into a lexicon of subclasses. While the current study is focused on dermatology-related images collected by a combined primary and tertiary care center, we expect similar approaches to aid a variety of institutions and clinical specialties.
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U2 - 10.1109/EMBC.2019.8857086
DO - 10.1109/EMBC.2019.8857086
M3 - Conference contribution
C2 - 31946860
AN - SCOPUS:85077894596
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4479
EP - 4482
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -