ICIAR 2020 - Invited Speakers
International Conference on Image Analysis and Recognition

17th International Conference on
Image Analysis and Recognition

ICIAR 2020
24-26 June, 2020 – Póvoa de Varzim, Portugal

Association for Image and Machine Intelligence

Invited Speakers

Deep learning and the Future of Radiology

Daniel Rueckert
Biomedical Image Analysis Group
Department of Computing
Imperial College London
London, UK

The talk will focus on the use of deep learning techniques for the discovery and quantification of clinically useful information from medical images. The talk will describe how deep learning can be used for the reconstruction of medical images from undersampled data, image super-resolution, image segmentation and image classification. It will also show the clinical utility of applications of deep learning for the interpretation of medical images in applications such as brain tumour segmentation, cardiac image analysis and applications in neonatal and fetal imaging. Finally, it will be discussed how deep learning may change the future of medical imaging.

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Towards Human-Friendly Explainable Artificial Intelligence

Hani Hagras
Computational Intelligence Professor
School of Computer Science and Electronic Engineering
University of Essex
Colchester, UK

The recent advances in computing power coupled with the rapid increases in the quantity of available data has led to a resurgence in the theory and applications of Artificial Intelligence (AI). However, the use of complex AI algorithms could result in a lack of transparency to users which is termed as black/opaque box models. Thus, for AI to be trusted and widely used by governments and industries, there is a need for greater transparency through the creation of human friendly explainable AI (XAI) systems. XAI aims to make machines understand the context and environment in which they operate, and over time build underlying explanatory models that allow them to characterize real-world phenomena. The XAI concept provides an explanation of individual decisions, enables understanding of overall strengths and weaknesses, and conveys an understanding of how the system will behave in the future and how to correct the system’s mistakes. In this keynote speech, Hani Hagras introduce the concepts of XAI by moving towards “explainable AI” (XAI) to achieve a significantly positive impact on communities and industries all over the world and will present novel techniques enabling to deliver human friendly XAI systems which could be easily understood, analysed and augmented by humans. This will allow to the wider deployment of AI systems which are trusted in various real world applications.

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Embedded computer vision and machine learning for drone imaging

Ioannis Pitas
Artificial Intelligence Information Analysis Laboratory
Department of Informatics
Aristotle University of Thessaloniki
Salonica, Greece

The aim of drone imaging is: a) to provide information (e.g., semantic 3D maps) for drone mission planning; b) to enhance drone perception for mission execution (e.g., to detect and track targets, avoid obstacle, detect emergency landing sites); c) to provide application dependent visual output. In the last case, it can be used e.g., to visually survey large expanses, ranging, for example, from a stadium to an entire city or employ computational cinematography techniques to create nice footage of sites and events. This keynote lecture will survey innovative intelligent single- and multiple-drone computer vison techniques to address these goals, notably human-centered ones: a) precise semantic 3D mapping using deep semantic image segmentation; b) deep learning for target detection and tracking (e.g., of a human performing a task); c) human activity recognition to detect abnormal events. For safety reasons, most of these tasks should be embedded on drone, using GPU and multicore CPU processing. Fast execution is important, particularly in case where deep video analysis is performed, having large computational load.

This lecture will offer an overview of current research efforts on all related topics, ranging from visual semantic segmentation/mapping to drone perception for autonomous target following, tracking and activity recognition.

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ICIAR - International Conference on Image Analysis and Recognition
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