Driving the Future of Healthcare and Life Sciences

At Intel, our decades-long history in healthcare and life sciences has given us deep insights into the needs of clinicians, researchers, and patients. We use this knowledge in combination with our expertise in AI, ubiquitous computing, pervasive connectivity, and edge-to-cloud capabilities to create technologies that help organizations overcome complex challenges and use data in more intelligent and effective ways.

With a vast hardware and software portfolio that supports a robust partner ecosystem, we’re powering the convergence of digital technologies into instruments, devices, and tools that can improve patient outcomes and experiences, accelerate scientific discoveries, and streamline clinical and lab workflows for providers and researchers. Intel® technology delivers the platform ubiquity and the performance, flexibility, and scalability needed to transform health and life sciences and help improve the life of every person on the planet.

Product and Performance Information

1

Yang, Shanling, et al. “Performance and Reading Time of Automated Breast US with or without Computer-Aided Detection.” Radiology 292, No. 3 (June 18, 2019): https://doi.org/10.1148/radiol.2019181816.

2

Jiang, Yulei, et al. “Interpretation Time Using a Concurrent-Read Computer-Aided Detection System for Automated Breast Ultrasound in Breast Cancer Screening of Women with Dense Breast Tissue.” American Journal of Roentgenology 211, No. 2 (August 2018): 452–461. https://www.ajronline.org/doi/10.2214/AJR.18.19516.

3

“TGen Unravels Genetic Mystery of Disease,” Intel, n.d. Accessed March 14, 2022. https://www.intel.com/content/www/us/en/customer-spotlight/stories/tgen-customer-story.html.

4

KFBIO cervical cancer screening OpenVINO model throughput performance on Intel® Xeon® Gold 6148 processor:

NEW:

Test 1: Tested by Intel as of 6/15/2019. Two-socket Intel® Xeon® Gold 6148 processor; 20 cores; HT: on; turbo: on; total memory: 192 GB (12 slots/16 GB/2,666 MHz); BIOS: SE5C620.86B.0X.01.0007.062120172125 (ucode: 0x200004d); CentOS Linux release 7.5.1804 (Core); deep learning framework: Keras 2.2.4 and Intel-optimized TensorFlow: 1.13.1; topology: RetinaNet: https://github.com/fizyr/keras-retinanet; compiler: gcc 4.8.5,MKL DNN version: v0.17, BS=8, both synthetic data and customer data; one instance/two socket; datatype: FP32.

Test 2: Tested by Intel as of 6/15/2019. Two-socket Intel® Xeon® Gold 6148 processor; 20 cores; HT: on; turbo: on; total memory: 192 GB (12 slots/16 GB/2,666 MHz); BIOS: SE5C620.86B.0X.01.0007.062120172125 (ucode: 0x200004d), CentOS Linux release 7.5.1804 (Core); Intel® software: OpenVINO R2019.1.1094; topology: RetinaNet: https://github.com/fizyr/keras-retinanet; compiler: gcc 4.8.5,MKL DNN version: v0.17, BS=1, eight asynchronous requests, both synthetic data and customer data; one instance/two socket; datatype: FP32.

BASELINE: Tested by Intel as of 6/15/2019. Two-socket Intel® Xeon® Gold 6148 processor; 20 cores; HT: on; turbo: on; total memory: 192 GB (12 slots/16 GB/2,666 MHz); BIOS: SE5C620.86B.0X.01.0007.062120172125 (ucode: 0x200004d), CentOS Linux release 7.5.1804 (Core); deep learning framework: Keras 2.2.4 and Vanilla TensorFlow: 1.5; topology: RetinaNet: https://github.com/fizyr/keras-retinanet; compiler: gcc 4.8.5,MKL DNN version: v0.17, BS=8, both synthetic data and customer data; one instance/two socket; datatype: FP32.

5

Performance claim based on internal Samsung testing as of March 2021. System configuration: Intel® Core™ i3-8100H CPU @ 3.0 GHz, 8 GB memory; OS: 64-bit Windows 10.