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2 March 2020 Cloud platform for deep learning-based CAD via collaboration between Japanese medical societies and institutes of informatics
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Abstract
Deep Learning-based medical imaging research has been actively conducted thanks to its high diagnostic accuracy comparable to that of expert physicians. However, to apply developed Computer Aided Diagnosis (CAD) systems to various data collected from different hospitals, we should prepare sufficient training data in terms of quality/quantity; unfortunately, especially in Japan, we need to overcome each hospital’s different ethical codes to obtain such multi-institutional data. Therefore, we built a cloud platform for (i) collecting multi-modal large- scale medical images from hospitals through medical societies and (ii) conducting various Deep Learning-based CAD research via collaboration between Japanese medical societies and institutes of informatics. Each hospital first provides the data to the corresponding medical society among 6 societies (e.g., Japan Radiological Society and Japanese Society of Pathology) based on their modality among 8 modalities (e.g., Computed Tomography and Whole Slide Imaging (WSI)); then, each society uploads them, possibly with annotation, to our cloud plat- form established in November 2017. We have collected over 80 million medical images by December 2019, and over 60 registered researchers have conducted CAD research on the platform. We presented the achieved results at major international conferences/in medical journals; their ongoing clinical applications include remote WSI diagnosis. We plan to further increase the number of images/modalities and apply our research results to a clinical environment.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kohei Murao, Youichirou Ninomiya, Changhee Han, Kento Aida, and Shin'ichi Satoh "Cloud platform for deep learning-based CAD via collaboration between Japanese medical societies and institutes of informatics", Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 113180T (2 March 2020); https://doi.org/10.1117/12.2543521
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