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Machine Learning for Preventing and Combating Future Pandemics

11 Oct 2021 10:26 AM | Anonymous

Healthcare AI has shown great potential in diagnosing and combating diseases such as diabetic retinopathy. Currently, to train a high performing deep learning model, a large number of high quality, accurately labeled data, as well as substantial computing resources, are needed. These data are unlikely to be found especially during early phases of a disease outbreak. Moreover, deep learning models can give overly confident predictions on out-of-distribution data, which may be misleading to the diagnosticians. We will describe two techniques to enable rapid identification of novel diseases like COVID-19 under limited data. The first technique addresses the challenge of label scarcity, and makes use of a conditional generative flow and a classifier for conditional synthetic data generation. The second technique helps detecting out-of-distribution inputs to a deep learningbased classification model. The model uses modern anomaly/outlier detection algorithms to analyze the outputs of a supervised classification model in order to identify potentially novel diseases. We present case studies using real-world medical image data and show the efficacy of proposed techniques.

29 October 2021

1300 – 1400h (UTC +8) 

Zoom Meeting Link: S0pzZlE2QT09

Meeting ID: 927 7144 3204

Passcode: 196792

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Room N3-02C-96, School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 639798

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