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Da Vinci — A scalable architecture for neural network computing (updated v3)

10/5/2020

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The updated version of this presentation provides additional information on the applicability of artificial intelligence in modern medicine, shows more insights into the end-to-end life cycle of AI implementations in projects and gives more details of our software stack.
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Prediction of protein subcellular localization

3/3/2020

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  • Use deep learning tools to accurately identify the organelles where proteins are located in human protein fluorescence micrographs
  • The trained model was executed on the Atlas 200 DK developer kit, and use Atlas 200 DK
  • The model analyses unlabelled protein fluorescence and predicts the location of sub-cells with pictures
  • Protein subcellular localisation prediction targets the microscopic fluorescence images of proteins in cancer tissues and other tissues to identify the localisation of proteins; to find location markers related to cancer
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Retinal blood vessel segmentation in the eyeground

3/3/2020

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  • The fundus retinal blood vessel segmentation application was developed for the Atlas 200 DK inference system, in partnership with the Nankai University, led by Professor Li Tao of Intelligent Computing System Research Office .
  • This project makes full use of the neural network computing power of the Atlas 200 DK system to segment the fundus vessels in real-time.
  • The total inference time of 20 pictures is 761.8 milliseconds, and the average inference time of one image is 38 milliseconds.
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Emerging technologies in medicine

29/2/2020

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​Artificial intelligence will shape our future like no other technology. Since the role of this technology is expanding, it will optimise and improve what people do. In the medical field, artificial intelligence will help support diagnostic processes and other related processes. To bring artificial intelligence to clinical relevance, the technology industry, the medical industry and physicians are required to solve complex challenges in an interdisciplinary approach.

This week I had the honour and pleasure to give a talk at the "Emerging Technologies in Medicine" conference, which brings experienced physicians, engineers and computer scientists together to talk about topics related to the future and exchange opinions for the challenges ahead. In my talk, I presented how Huawei supports the health care industry with artificial intelligence and described the process of how we execute AI projects. I described the challenges of microprocessor trends and introduced computer architectural approaches to solve these challenges, and showed Huawei's rich AI product portfolio.  I also showed where AI, specifically with our products, has been successfully used in medical research such as in retinal blood vessel segmentation in the eyeground and the prediction of protein subcellular localisation. 

There are still challenges ahead, but with collaborative approaches such as this, I think we will be better equipped in facing our future.
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