The new X-Ray model is deployed on Xilinx Zynq UltraScale+ MPSoC device based ZCU104, and leverages the Xilinx deep learning processor unit (DPU)
The companies jointly developed solutions that leverage an open-source model. (Credit: Marc Manhart from Pixabay)
Xilinx, in partnership with Spline.AI, has launched a fully functional medical X-ray classification deep-learning model and a reference design kit on Amazon Web Services (AWS).
The US-based technology company said that its new model is deployed on Xilinx Zynq UltraScale+ MPSoC device based ZCU104 and leverages the Xilinx deep learning processor unit (DPU).
Xilinx DPU is a soft-IP tensor accelerator, which is capable of running different neural networks, including classification and detection of diseases.
Xilinx marketing and business development vice president Kapil Shankar said: “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.
“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.”
Under the partnership, the companies jointly developed solutions that leverage an open-source model, running on a Xilinx Zynq UltraScale+ MPSoC device, on a Python programming platform.
The solutions developed under the partnership can be used by the researchers for different applications and adapted for specific requirements, said the company.
The open-source design can be used by medical diagnostic, clinical equipment makers and healthcare service providers, for various clinical and radiological applications in a mobile, portable or point-of-care edge device with the option to scale using the cloud.
The AI model is trained using Amazon SageMaker and is deployed using AWS IoT Greengrass
The artificial intelligence (AI) model of the solution is trained using Amazon SageMaker and is deployed from cloud to edge using AWS IoT Greengrass.
The solution is said to facilitate remote machine learning (ML) model updates, geographically distributed inference, and the capability to scale across remote networks and large geographies.
Amazon Web Services IoT vice president Dirk Didascalou said: “We are delighted to support Xilinx design a solution for healthcare customers who are in need of ways to rapidly develop trained models for clinical and radiological applications.
“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. 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.”
The new solution is used for pneumonia and Covid-19 detection system
The company said that its new solution has been used for a pneumonia and Covid-19 detection system, with high levels of accuracy and low inference latency.
To train the deep learning models, the team leveraged more than 30,000 curated and labelled pneumonia images and 500 Covid-19 images .
Spline.ai is an advanced technology company that develops healthcare tools and products by leveraging AI and high-performance computing platforms.
Spline.AI CTO Syed Hussain said: “Xilinx Zynq UltraScale+ MPSoCs are Edge devices ideally suited for scalable deployment of high-performance deep-learning models in a clinical setting, such as the new COVID-XS model that we worked to train and develop for this collaborative effort.”