What are the challenges with using Machine Learning in Medical Imaging?
Many procedures within radiology, pathology, dermatology, vascular diagnostic and ophthalmology could be on large image sizes, sometimes five Megapixels or larger, requiring complex image processing. Also, the ML workflow can be computing and memory intensive. The predominant computation is linear algebra and demands many computations and a multitude of parameters.
This results in billions of multiply-accumulate (MAC) operations, hundreds of Megabytes of parameter data and requires a multitude of operators and a highly-distributed memory subsystem. So, performing accurate image inferences efficiently for tissue detection or classification using traditional computational methods on PCs and GPUs are inefficient, and healthcare companies are looking for alternate techniques to address this problem.
What does Xilinx offer for Machine Learning in Medical Imaging?
Xilinx technology offers a heterogenous and a highly distributed architecture to solve this problem for medical imaging companies. Xilinx Versal™ Adaptive Compute Acceleration Platform (ACAP) family of System-on-Chips (SoCs) with its adaptable Field Programmable Gate Arrays (FPGAs), integrated digital signal processors (DSPs), integrated accelerators for deep learning, SIMD VLIW engines with a highly distributed local memory architecture and multi-processor systems are known for their ability to perform massively parallel signal processing of high-speed data in close to real-time.
I am not a hardware developer; how does this help me?
Xilinx has an innovative ecosystem for algorithm and application developers. Unified software platforms, such as Vitis™ for application development and Vitis AI™ for optimising and deploying accelerated ML inference, mean developers can use advanced devices – such as ACAPs – in their projects.