Image analysis and modeling in medical image computing

Machine learning and neural networks are successfully applied in various regression and classification problems. Even though neural network models are used to make diagnosis based on medical images, there are still some areas where machine learning due to complexity of the problem has not been applied. One of those areas is ultrasound image analysis. The main distinct feature of this analysis is that the objects in the images are noisy and lack for clear edges. Moreover, final diagnosis requires an analysis of sequence of images. The fact that the interpretation of ultrasound images is not trivial exercise even for experts makes this problem a perfect candidate for the automation. In this paper, neural network capabilities are examined in the context of analysis and diagnosis based on ultrasound images. In order to compare results, paper focus on the heart echoscopy images and cardiovascular disease diagnostic problem. It has been shown that state-of-the-art convolutional neural networks trained with static images cannot be successful in making prediction based on ultrasound images. As a result, specific neural network architecture has been designed. This network showed accuracy of 75% and proved that neural networks can be successfully applied in making diagnosis based on ultrasound images

Progress on machine and deep learning applications in CMS computing

Machine and Deep Learning techniques are being used in various areas of CMS operations at the LHC collider, like data taking, monitoring, processing and physics analysis. A review a few selected use cases - with focus on CMS software and computing - shows the progress in the field, with highlight on most recent developments, as well as an outlook to future applications in LHC Run III and towards the High-Luminosity LHC phase