This white paper describes how LEPU Medical leverages Intel solutions to accelerate artificial intelligence (AI) deployment in next-generation digitized electrocardiogram (ECG) diagnostic devices and services.
This Solution Brief illustrates how the JianPei Tech LTD AI-based platform accelerates image reading performance launching innovative medical diagnostic systems across china.
This Solution Brief illustrates how Arya.ai provides a relatively simple and cost-effective pathway for financial services organizations and other enterprise customers to integrate AI into their business models.
This Solution Brief explains how an integrated solution advances artificial intelligence (AI) with development with H2O.ai's industry-leading software, validated and benchmarked on optimized Intel® technologies.
This Solution Brief highlights how engineers and developers at HOOBOX are using hardware and software technologies from the Intel® artificial intelligence portfolio to optimize their algorithms that detect and interpret facial expressions, giving tetraplegic (quadriplegic) individuals new autonomy to control their wheelchairs with just a glance.
In this joint work, DellEMC, SURFsara, and Intel extended the research using VGG-16 and ResNet-50 CNN models scaled out across a large number of Intel® Xeon® Scalable processors running on Dell EMC’s Zenith supercomputer and accurately pre-trained on the ImageNet2012-1K dataset. The team was able to significantly reduce the training time and outperform the CheXNet-121 published results in four pathological categories using VGG-16 and up to 10 categories (including pneumonia and emphysema).
This document explores deep learning systems for image recognition in health and life sciences (HLS) and how Intel’s portfolio of products for artificial intelligence (AI) is helping make HLS AI solutions a reality.
This is an educational white paper on transfer learning, showcasing how existing deep learning models can be easily and flexibly customized to solve new problems. One of the biggest challenges with deep learning is the large number of labeled data points that are required to train the deep learning models to sufficient accuracy.
This paper compares the performance of Intel® Distribution for Python* to that of non-optimized Python using a breast cancer classification. This comparison was done using machine learning algorithms from the scikit-learn* package in Python.
The Intel® Pharma Analytics Platform offers continuous, remote patient monitoring and artificial intelligence to help streamline clinical trials, reduce costs, and deliver fresh insights for drug development.