AI to Predict Parkinson

Looks like artifical intelligece can be used for really important things
16 November 2018   617

In Oxford, an AI-framework for the diagnosis of nystagmus is created - an early symptom of neurodegenerative pathologies, such as Parkinson's disease. Nystagmus is a form of sleep disturbance, a series of involuntary rapid tremors in the eyeballs of a sleeping person. Rapid diagnosis of nystagmus will allow to treat Parkinson’s disease at an early stage.

The researchers used data from 53 patients from an open laboratory database of the Montreal Sleep Research Archive. Records of electrical activity of the brain, skeletal muscles and eye movements were processed using the algorithm of regression decision trees (random forest).

As the main symptom of nystagmus and the approaching Parkinson's disease, researchers considered muscle atony. In total, electrograms identified 156 different features that can indicate the development of pathology.

Scientists used manual and automatic markup methods for a data set. With manual marking, they managed to achieve diagnostic accuracy of 96%, with automatic results being 4% worse. The researchers plan to improve the results of automatic processing using mathematical functions that mimic the behavior of brain neurons.

A month before the publication of the work of experts at Oxford University, scientists from the Swiss Institute of IRIS reported on the results of work on their own system for diagnosing neuropathology. The fundamental difference is that the Swiss system uses data collected using a smartphone, and the development from Oxford relies on special medical tests.

Nvidia to Open StyleGan Source Code

This machine learning project allows to create of people faces by imitating photographs
11 February 2019   640

NVIDIA has open source code if developments related to the StyleGAN project, which allows generating images of new faces of people by imitating photographs. The system automatically takes into account aspects of the placement of individuals and makes the result indistinguishable from real photos (most of the respondents could not distinguish the original photos from the generated ones). For the synthesis of individuals, a machine learning system based on a generative-competitive neural network (GAN) is used. The code is written in Python using the TensorFlow framework and published under the Creative Commons BY-NC 4.0 license (for non-commercial use only).

Both ready-made trained models and collections of images for self-learning of a neural network are available for download. The basic model was trained on the basis of the Flickr-Faces-HQ (FFHQ) collection, which includes 70,000 high-quality (1024x1024) PNG images of people's faces. At the same time, the system is not tied to persons - as an example, the variants trained on collections of photographs of cars, cats and beds are shown. It requires one or more NVIDIA graphics cards (Tesla V100 GPU recommended), at least 11 GB of RAM, NVIDIA 391.35+ drivers, CUDA 9.0+ tools and the cuDNN 7.3.1 library.

The system allows you to synthesize the image of a new face based on interpolation of features of several faces, combining features characteristic of them, as well as adapting the final image to the required age, gender, hair length, smile character, nose shape, skin color, glasses, face rotation in the photo. The generator considers the image as a collection of styles, automatically separates the characteristic details (freckles, hair, glasses) from common high-level attributes (posture, gender, age changes) and allows you to combine them in an arbitrary form with the definition of the dominant properties through weights.