Zero shot augmentation learning in internet of biometric things for health signal processing
یادگیری تقویتی صفر در اینترنت اشیا بیومتریک برای پردازش سیگنال سلامتی-2021
In recent years, the number of Internet of Things (IoT) devices has increased rapidly. The Internet of Biometric Things (IoBT) can process biometrics and health signals, and it will greatly extend the range of biometric applications. The analysis of health signals in the IoBT can use computer-aided diagnosis techniques. However, most of the existing computer-aided diagnosis methods are developed for common diseases and are not suitable for rare diseases. Zero shot learning is a potential method for the computer- aided diagnosis of rare diseases because it can identify objects of unknown categories. However, the ex- isting zero shot learning methods are based on attribute learning and rely on an attribute dataset. There is no attribute dataset for health signal processing. Therefore, the existing zero shot learning methods are not suitable for health signal processing. Based on the above background, we propose a zero shot aug- mentation learning model (ZSAL) in the IoBT for health signal processing. First, an expert doctor identiﬁes the contour of a lesion and selects a background image without a lesion. Second, the computer automatically generates virtual images using zero shot augmentation technology. Finally, the generated virtual dataset is used to train a convolutional classiﬁer, and then we apply the classiﬁer to the computer-aided diagnosis of actual medical images. The experiment shows the eﬃciency and effectiveness of our method.© 2021 Elsevier B.V. All rights reserved.
Keywords: Internet of biometric things | Zero shot learning | Data augmentation | Health signal processing