Anomaly Detection in Radiological Images Using Deep Learning
Deep learning is usually linked to Big Data. However, in a medical context radiologists often need help in diagnosing rare conditions with limited historical data. Can Deep Learning prove a useful tool in such cases? COSMONiO is designing NOUS, a deep neural network platform that aims to make high-accuracy predictions using significantly smaller training datasets generated from X-ray, CT, MRI and PET scanners. We will discuss the main challenges and how we address them.
Ioannis is an engineer with a PhD degree in real-time computer vision from Cranfield University, UK. He founded COSMONiO HEALTHLAB in 2015 with a vision to develop intelligent medical technologies based on deep learning. COSMONiO's focus is on designing NOUS, an embedded deep neural network platform that aims to make high-accuracy predictions using significantly smaller training datasets. To achieve this COSMONiO collaborates closely with UMC Groningen, one of the leading university hospitals in The Netherlands. Their research is currently focussing on performing automated anomaly detection on thoracic X-rays.