Xilinx, Spline.AI and Amazon team up on ML x-ray classification: Page 2 of 3

October 14, 2020 //By Nick Flaherty
Xilinx teams for cloud-based open source Covid-19 X-ray classification
Xilinx has teamed with Spline.AI and Amazon on a cloud-based adaptive machine learning model that can recognise different lung infections from X-rays
few months. This gives a faster development path for inferencing, edge device sand allows them to scale using the cloud and it will run on a portable device such as a tablet,” he added.

The open-source model runs on a Python programming platform on a Xilinx Zynq UltraScale+ MPSoC device, meaning it can be adapted by researchers to suit different application specific requirements. Medical diagnostic, clinical equipment makers and healthcare service providers can use the open-source design to rapidly develop and deploy trained models for many clinical and radiological applications in a mobile, portable or point-of-care edge device with the option to scale using the cloud.

“AI is one of the fastest growing and high demand application areas of healthcare, so we’re excited to share this adaptable, open-source solution with the industry,” said Kapil Shankar, vice president of marketing and business development, Core Markets Group at Xilinx. “The cost-effective solution offers low latency, power efficiency, and scalability. Plus, as the model can be easily adapted to similar clinical and diagnostic applications, medical equipment makers and healthcare providers are empowered to swiftly develop future clinical and radiological applications using the reference design kit.”

“Amazon SageMaker enabled Xilinx and Spline.AI to develop a high-quality solution that can support highly accurate clinical diagnostics using low cost medical appliances," said Dirk Didascalou, Vice President of IoT at AWS. "The integration of AWS IoT Greengrass enables physicians to easily upload X-ray images to the cloud without the need of a physical medical device, enabling physicians to extend the delivery care to more remote locations.”

www.xilinx.com/applications/medical/healthcare-ai.html

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