Data-intensive solutions for medical technologies

Summary

This project aims at developing advanced solutions in software for processing medical data, addressing the issue of less or no labelled data.

Supervisor(s)

Dr Omid Kavehei

Research Location

Electrical and Information Engineering

Program Type

Masters/PHD

Synopsis

Using today's advances in machine intelligence and pattern recognition, and our incredibly massive amount of structured and unstructured data on central nervous systems and the Brain, making sense of massive datasets with high amount of data-noise and incoherency is now a possibility [1-3]. We will develop, test and implement cognitive computing technologies in data-driven medical contexts. This project aims to develop data-driven machine learning medical technologies to make medical practices more personalized and precision in both domains of medical devices and services. While expanding knowledge in the information and computing sciences, this project aims to massively reduce costs in health and support services as well as providing low-cost bed-side or wearable technologies for constant monitoring and notification systems.This project uses our state-of-the-art GPU cluster to develop these technologies. 
References:
[1]. Shen, Dinggang, Guorong Wu, and Heung-Il Suk. "Deep learning in medical image analysis." Annual Review of Biomedical Engineering 0 (2017).
[2]. Truong, Nhan Duy, et al. "Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection." Expert Systems with Applications (2017).
[3]. Truong, Nhan Duy, et al. "A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis." arXiv preprint arXiv:1707.01976 (2017).

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Keywords

portable electronics, biomedical engineering, www.deepnano.ai

Opportunity ID

The opportunity ID for this research opportunity is: 2392

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