Chip maker NXP Semiconductor is developing a neural networking technology that can identify features in chest Xrays to diagnose Covid-19.
The xAI neural network adds clearly defined errors into Xrays to create uncertainty that improves the accuracy of the network training. This is currently being tested on several neural network frameworks, including Google’s TensorFlow.
"With the unprecedented global Covid-19 pandemic situation, our xAI research teams believe that xAI might help enable the rapid detection of the disease in patients. It is still early days, but we are encouraged by the proof points we have seen," said Lars Regers, CTO of NXP. "The use of CT radiology and X-ray imaging provides a fast alternative detection capability alongside the prescribed PCR testing and diagnosis protocols. CT and X-ray images could be processed by a suitably trained xAI model to differentiate between clean and infected cases. xAI allows for real-time inference confidence and explainable insights to aid clinical staff in determining the next stage of treatment."
“Today we have multiple techniques for introducing the uncertainty, but we fit into TensorFlow as it fits into the bigger environment,” said Gowri Chindalore, Head of Technology & Business Strategy for Embedded Processors. The technique is helpful for improving the accuracy of machine learning (ML) algorithms not just for image recognition for Xrays but also for automotive ADAS and autonomous driving.
“This is at an early stage of development we are applying all three techniques to the data we have, such as traffic signs as well as Xrays, and we are looking at what lends itself best for model optimisation, ” said Chindalore. “Not every application really needs this uncertainty estimation, the majority of ML applications do well with an inference engine.”
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