Twin Seminars on Big Data in Radiology & Medicine

Twin Seminars on Big Data in Radiology & Medicine

Date: 9 December 2016

Speaker: Dr. Allan Hanbury, Vienna University of Technology

Seminar 1: Insights from Medical Text and Image Data

Time: 10 am – 12 pm

Venue: Seminar Room, Department of Radiology, UKM Medical Center, Cheras, Kuala Lumpur

Abstract:

Since 2010, the European Commission has been supporting the creation of analysis and search tools for unstructured, multimodal, multilingual medical data. For image data, this technology makes radiology images searchable by visual similarity, to assist radiologists in understanding the images. For text data, the tools are also being used in an NHS hospital to learn about trends in patient histories, and in two medical web search engines to add multilingual capabilities and estimate the trustworthiness of medical websites. This talk will present challenges in analysing unstructured medical text and image data, outline the main tools, describe some of the applications in place, and present a few opportunities available in the unstructured medical data analysis area.

Seminar 2: Bringing the Algorithms to the Data: Evaluation-as-a-Service and Living Labs

Time: 3 – 5 pm

Venue: Meeting Room 1, Block A, Centre for Artificial Intelligence Technology, Faculty of Information Science & Technology, UKM, Bangi

Abstract:

Carrying out Data Science experiments usually requires downloading data so that the experiments are performed locally. However, distributing data is often not practical, as the data may be: (i) Huge – in order to obtain realistic results, the experiments should be done on realistic amounts of data, which could be Petabytes. (ii) Non-distributable – in many cases, it is not permitted to distribute data due to privacy, terms of service, or commercial sensitivity of the data. (iii) Real-time – companies with systems generating data in real-time often find experimental results obtained on static historical static data of little practical use. This talk discusses two paradigms, Evaluation-as-a-Service and Living Labs, being developed to give access to data while removing the necessity to download, or even to see, the data. Practical examples of using these paradigms will be given, in particular from the VISCERAL project on evaluation of medical imaging and retrieval algorithms.
Registration: http://bit.ly/BDRM091216

Speaker: Dr. Allan Hanbury, Vienna University of Technology

Allan Hanbury is Senior Researcher and Privatdozent at the TU Wien, Austria. He is coordinator of the EU-funded KConnect Innovation Action on technology for analysing medical text, and coordinator of the CHIST-ERA project MUCKE on credibility of and search in multimodal data and social networks. He was scientific coordinator of the EU-funded KHRESMOI Integrated Project on medical and health information search and analysis, and coordinator of the EU-funded VISCERAL project on evaluation of algorithms on big data. He was leader of the Evaluation, Integration and Standards work package of the MUSCLE EU Network of Excellence, and has led a number of Austrian national projects. His research interests include data science, information retrieval, multimodal information retrieval, and the evaluation of information retrieval systems and algorithms. He is author or co-author of over 100 publications in refereed journals and refereed international conferences.

He was born in George, South Africa in 1974. He was awarded the B.Sc. degree in Physics and Applied Mathematics in 1994, the B.Sc. (Hons) degree in Physics in 1995, and the M.Sc. degree in Physics in 1999, all from the University of Cape Town, South Africa. He was awarded the Ph.D. degree in Applied Mathematics in 2002 from the Mines ParisTech, France, and the Habilitation in Practical Informatics from the TU Wien, Austria, in 2008

Best regards,

Ashrani Aizzuddin Abd Rahni, Ph.D.,
IEEE Member No. 90267216
Department of Electrical, Electronic & Systems Engineering,
Faculty of Engineering & Built Environment,
Universiti Kebangsaan Malaysia

Tel.: 03-89216301
Mob.: 019-3150847

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