This article was originally published on March 1, 2020.
Editor’s Note: This content is contributed by Subhankar Bhattacharya, Marketing Lead for Healthcare Sciences & Medical Devices.
The use of artificial intelligence (AI) – including machine learning (ML) and deep learning techniques (DL) is poised to become a transformational force in medical imaging. Patients, healthcare service providers, hospitals, professionals, and various stakeholders in the ecosystem all stand to benefit from ML driven tools. From anatomical geometric measurements to cancer detection, the possibilities are endless. In these scenarios, ML can lead to increased operational efficiencies and generate positive outcomes.
There’s a broad spectrum of ways that ML can be used in medical imaging. For example, radiology, dermatology, vascular diagnostics, digital pathology and ophthalmology all use standard image processing techniques.
In radiology, chest x-rays are the most common radiological procedure with over 2 billion scans performed worldwide every year, that’s a staggering 548,000 scans a day. Such a huge quantity of scans imposes a heavy load on radiologists and taxes the efficiency of the workflow. Though the expertise of a radiologist is still of paramount importance, often ML, Deep Neural Network (DNN), and Convolutional Neural Networks (CNN) methods outperform radiologists in speed and accuracy. Under stressful conditions during a fast decision-making process, the human error rate could be as high as 30%. Aiding the decision-making process with ML methods can improve the quality of results, providing the radiologists and other specialists an additional tool.
Regulatory support is steadily increasing and the US Federal Drug Administration (FDA) is approving more and more ML methods for diagnostic assistance and other applications. The FDA has also created a new regulatory framework for ML based products. This new framework refers to ML techniques as “Software as a Medical Device” (SaMD) and envisions significant benefits to quality and efficiency of care.
The major challenges to implement Machine Learning in Medical Imaging
Many procedures within radiology, pathology, dermatology, vascular diagnostic and ophthalmology could be on large image sizes, sometimes 5 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.
So, what does Xilinx offer for Machine Learning in Medical Imaging?
Xilinx offers a heterogenous and a highly distributed architecture to solve this problem for healthcare companies. Xilinx Versal™ Adaptive Compute Acceleration Platform (ACAP) family of multi-processor System-on-Chips (SoCs) with its integrated accelerators for deep learning and its SIMD VLIW ‘AI’ engines are known for their ability to perform massively parallel signal processing of high-speed data in close to real-time. This means computing capacity can be moved beyond 100 Tera operations per second (TOPS).
These devices dramatically improve the efficiency of how complex healthcare ML algorithms are solved and help to significantly accelerate healthcare applications at the edge, all with less resources, cost and power. With Versal ACAP devices, support for recurrent networks could be inherent due to the simple nature of the architecture and its supporting libraries.
Xilinx has an innovative ecosystem for algorithm and application developers. Unified software platforms, such as Vitis™ for application development and Vitis AI™ for optimizing and deploying accelerated ML inference, mean developers can use advanced devices – such as ACAPs - in their projects.
Healthcare and medical device workflows are undergoing major changes. In the future, medical workflows will be ‘Big Data’ enterprises with significantly higher requirements for computational needs, data privacy, security, patient safety and accuracy. Distributed, non-linear, parallel and heterogeneous computing platforms are key for solving and managing this complexity. Xilinx devices like Versal and the Vitis software platform are ideal for delivering the optimized AI architectures of the future.
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