Xilinx teams for cloud-based open source Covid-19 X-ray classification
Xilinx has launched a fully functional medical X-ray classification deep-learning open source model and a reference design kit running on its FPGAs on Amazon Web Services (AWS). This can allow medical equipment makers to help improve diagnostics of a range of repiratory infections from pneumonia to Covid-19
The model was developed with Spline.AI and Amazon Web Services (AWS) GreenGrass IoT service. The model is deployed on the Xilinx Zynq UltraScale+ MPSoC device based ZCU10. It makes use of the Xilinx deep learning processor unit (DPU), a soft-IP tensor accelerator, to run a variety of neural networks, including classification and detection of diseases.
“We were working closely with Spline.ai before Covid hit when were working on an X-ray classification model for pneumonia and then we started on a reference design kit and then we started looking at potential data for Covid and an open source model on Github,” said Subhankar Bhattacharya, Lead for Healthcare & Sciences at Xilinx.
“We managed to acquire a curated dataset from a number of sources to distinguish between pneumonia and other diseases and then we added Covid-19,” he said. “That was extended to include AWS Greengrass for scalability and the reference design kit allows medical equipment makers to create other platforms with different radiological models.”
The data came from public research by healthcare and research institutes such as National Institute of Health (NIH), Stanford University, and MIT, as well as other hospitals and clinics around the world.
“Right now it supports AWS GreenGrass but it could be in other cloud providers or purely and edge solution. It could also be complied entirely under Vitis and fed in the flow and retrain as more data comes along,” he said.
“This uses the VC104 as the edge device and the model has been trained with 30,000 images for pneumonia and 500 for Covid-19 and we plan to update that every 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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