健培科技在医疗影像AI领域持续创造出色的成绩，除了自身强大的研发实力之外，还得益于各大合作单位的通力协作。

近日，英特尔官网公布了与健培科技进行技术攻关的成绩单，仅基于人工智能平台的图像读取性能上就提升了8倍。

（图为英特尔官网全球报道）

全文翻译

In China, Artificial Intelligence (AI) has deeply penetrated many industries, including medical care. This is motivated by the fact that 80 percent of medical data in China is imaging, which requires analysis and diagnosis based on the patient scans.

在中国，人工智能已经深入到包括医疗在内的许多行业。医疗数据中，80%都是影像数据，而影像数据需要根据患者扫描结果进行分析和诊断。

China faces two challenges with image processing: 1) there are not enough practitioners for human review to keep up with the growth of image volume, which currently expands by 30 percent per year; and 2) current computer-aided analysis lacks accuracy; manual review and subjective interpretation is inevitable.

中国在医疗影像处理方面面临两大挑战：1.没有足够的从业人员进行人体检查以满足图像量的增长，目前图像量每年以30%的速度增长；2.目前的计算机辅助分析的准确性尚未获得验证；人工审查不可避免。

AI in Medical Imaging Science

医疗影像学人工智能

The application of AI inferencing in medical imaging is complicated, requiring powerful processing capabilities for such challenges as data diversification, deep analysis, and complicated labeling. Medical image analysis requires support of 3D?or even 4D?deep neural network (DNN) architectures, which rely heavily on platform memory during processing. GPUs are often unable to handle the tremendous number of workloads required to process 3D and 4D image data. Therefore, medical teams may reduce the pixels of 3D image data and split them into multiple small blocks for sequential recognition. The TensorFlow* deep learning framework used to train algorithms on imaging data can benefit from optimizations for Intel? Xeon? Scalable processors.

将人工智能应用于医疗影像推理是非常复杂的，需要强大的处理能力来应对数据多样化、深度学习和复杂标记等挑战。医疗影像分析需要支持3D甚至4D深度神经网络（DNN）体系结构，在处理过程中严重依赖于平台内存。GPU通常无法满足处理3D和4D影像数据时所需的大量工作负载。因此，医疗团队通常采用减少3D影像数据的像素，并将其分割成多个小的影像块进行顺序识别。用于训练成像数据算法的TensorFlow*深度学习框架可以从英特尔至强可扩展处理器的优化中获益。

The Intel Xeon Scalable processor family is well-suited for deep learning applications. It can directly read up to 384 GB of memory to accommodate fast access to imaging data for inferencing. Thus, it can better meet the requirements of AI-based CT image analysis compared to other AI processing technologies.

英特尔可扩展处理器系列非常适合深度学习应用程序。它可以直接读取高达384GB的内存，以便快速访问影像数据进行推理。因此，与其他人工智能处理技术相比，它能更好地满足基于人工智能的CT图像分析的要求。

Intel? Optimization for TensorFlow* leverages the Intel? Math Kernel Library for Deep Neural Networks (Intel? MKL-DNN) to help improve performance on image data processing. Plus, it has customized 5,000 new features to provide better support for medical image analysis issues.

英特尔TensorFlow*优化利用英特尔深度神经网络的核心库（英特尔MKL-DNN）帮助提高影像数据处理性能。此外，它还定制了5000个新功能，以更好地支持医学图像分析问题。

Introducing an Image-Reading Robot

介绍一种影像阅片机器人

JianPei Tech LTD’s image analysis robot is built on advanced AI algorithms and deep learning technologies, enabling highly accurate image recognition. Assisting doctors in locating diseases, analyzing conditions, and guiding operations, the image reading robot is part of a clinical decision-making system and is at the frontier of medical science and medical technology development within the country. With the AI-enabled robot, the time needed to complete radiologist diagnoses have reduced immensely. This application has also substantially increased diagnostic accuracy using Intel optimizations.Such impact can advance precision medicine greatly in China.

在中国，人工智能在医疗领域的应用从手术机器人、医学影像诊断到远程医疗等细分领域经历了从无到有，从小到大的跨越式的发展。人工智能对医疗行业不仅仅是颠覆，更多的是创新。健培科技与英特尔合作实际上就是对医疗行业的一次颠覆性创新。在现实应用中，健培阅片机器人的诊断速度、准确率等主要指标均处于行业领先地位。它的出现，除了提高医生的工作效率外，还将作为辅助诊断，大大提高诊断的效率和准确率。健培阅片机器人可以协助医生定位疾病、分析病情、指导手术，是临床决策系统的一部分，处在国内医学科学和医学技术发展的前沿地位。

Accelerating AI-Enabled Imaging Analytics and Diagnoses

加速人工智能的图像分析和诊断

Intel worked with JianPei Tech to optimize their image- reading robot on Intel Xeon Scalable processors for inferencing of X-ray, CT, MRI, and other medical imaging sources. To accelerate image analysis, JianPei Tech’s plat-form was migrated to Intel? Xeon? Gold 6140 processors and optimized with Intel Optimization for TensorFlow, which includes the Intel MKL-DNN library.

英特尔与健培科技合作，优化英特尔至强可扩展处理器上的影像阅片机器人，以推理X射线、CT、MRI和其他医学成像数据。为了加快图像分析，健培的平台被移植到英特尔?至强?金 6140处理器，并使用英特尔? TensorFlow优化库（包括英特尔? MKL-DNN库）进行优化。

As shown in Figure 1, the collaborative work resulted in achieving an 8X performance improvement on DICOM (Digital Imaging and Communications in Medicine) images (identified as DCM in Figure 1).1 The speedups were a result of using Faster R-CNN* (Region Convolutional Neural Network) instead of UNet* and optimizing the Faster R-CNN algorithms for Intel Xeon Scalable processors.

如图1所示，协作工作使DICOM（医学数字成像和通信）图像（如图1所示为DCM）的性能提高了8倍。

英特尔?TensorFlow优化推理吞吐量与英特尔?志强?金 6140处理器上的UNET和自定义Faster R-CNN相比

图1使用优化的Faster R-CNN算法推理性能吞吐量

Faster R-CNN has also played a role in segmentation analytics in the medical field. Faster R-CNN is well optimized by Intel Optimization for TensorFlow and Intel MKL-DNN, which results in a performance gain of 6X compared to UNet* (see Figure 2).

Faster R-CNN也在医学领域的细分分析中发挥了作用。Faster R-CNN通过英特尔针对TensorFlow和英特尔MKL-DNN的优化得到了很好的优化，与UNET*相比，其性能提高了6倍（见图2）。

英特尔?TensorFlow优化推理吞吐量与在英特尔?至强?金6140处理器上自定义Faster R-CNN对比

图2与英特尔?至强?金 6140处理器上的UNET相比，被TensorFlow优化的Faster R-CNN推理性能优化更快

Optimum memory capacity and image data batch size also enhanced processing performance for faster R-CNN as shown in Figure 3.

最佳的内存容量和图像数据批量大小也提高了Faster R-CNN的处理性能，如图3所示。

英特尔?TensorFlow优化推理吞吐量与英特尔?至强?金 6140处理器上不同批量大小的自定义Faster R-CNN相比