Volume 12 Issue 5
Sep.  2021
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LI Xirong. Multi-modal Deep Learning and Its Applications in Ophthalmic Artificial Intelligence[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 602-607. doi: 10.12290/xhyxzz.2021-0500
Citation: LI Xirong. Multi-modal Deep Learning and Its Applications in Ophthalmic Artificial Intelligence[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 602-607. doi: 10.12290/xhyxzz.2021-0500

Multi-modal Deep Learning and Its Applications in Ophthalmic Artificial Intelligence

doi: 10.12290/xhyxzz.2021-0500

Beijing Natural Science Foundation 4202033

Beijing Natural Science Foundation Haidian Original InnovationJoint Fund 19L2062

the Pharmaceutical Collaborative Innovation Research Project of Beijing Science and Technology Commission Z191100007719002

More Information
  • Corresponding author: LI Xirong  Tel: 86-10-82504345, E-mail: xirong@ruc.edu.cn
  • Received Date: 2021-06-28
  • Accepted Date: 2021-07-29
  • Available Online: 2021-08-19
  • Publish Date: 2021-09-30
  • Deep learning, for its powerful learning capability and high usability, has been a prevalent algorithm of machine learning and a core technique for artificial intelligence(AI) in medicine and healthcare. Due to the importance of medical imaging in many tasks such as health screening, disease diagnosis, precise treatment, and prognosis prediction, deep learning of structural analysis and semantic understanding for medical images is becoming an important interdisciplinary research direction. In clinical scenarios, in order to achieve a more accurate diagnosis, doctors need to simultaneously refer to multiple modalities of medical imaging for a comprehensive analysis and judgment. This article introduced the basic concepts and working principles of multimodal deep learning in such scenarios, reviewed recent research progress on applying multi-modal deep learning in both generic medical fields and ophthalmology, and discussed technical challenges and also envision potential applications of multi-modal deep learning in AI-assisted ophthalmology.
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