AI to Predict Parkinson

Looks like artifical intelligece can be used for really important things
16 November 2018   864

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 SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   656

NVIDIA has released the source code for the SPADE machine learning system (GauGAN), which allows for the synthesis of realistic landscapes based on rough sketches, as well as training models associated with the project. The system was demonstrated in March at the GTC 2019 conference, but the code was published only yesterday. The developments are open under the non-free license CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0), allowing use only for non-commercial purposes. The code is written in Python using the PyTorch framework.

Sketches are drawn up in the form of a segmented map that determines the placement of exemplary objects on the scene. The nature of the generated objects is set using color labels. For example, a blue fill turns into sky, blue into water, dark green into trees, light green into grass, light brown into stones, dark brown into mountains, gray into snow, a brown line into a road, and a blue line into the river. Additionally, based on the choice of reference images, the overall style of the composition and the time of day are determined. The proposed tool for creating virtual worlds can be useful to a wide range of specialists, from architects and urban planners to game developers and landscape designers.

Objects are synthesized by a generative-adversarial neural network (GAN), which, based on a schematic segmented map, creates realistic images by borrowing parts from a model previously trained on several million photographs. In contrast to the previously developed systems of image synthesis, the proposed method is based on the use of adaptive spatial transformation followed by transformation based on machine learning. Processing a segmented map instead of semantic markup allows you to achieve an exact match of the result and control the style.

To achieve realism, two competing neural networks are used: the generator and the discriminator (Discriminator). The generator generates images based on mixing elements of real photos, and the discriminator identifies possible deviations from real images. As a result, a feedback is formed, on the basis of which the generator begins to assemble more and more qualitative samples, until the discriminator ceases to distinguish them from the real ones.