Alex Frangi at Spring Meeting 2022: Deep Understanding

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Precision Imaging – from model-based imaging to image-based modelling

by Prof. Alex Frangi (Schools of Computing and Medicine, University of Leeds, UK)

Medical image analysis has grown into a matured field challenged by the progress made across all medical imaging technologies and more recent breakthroughs in biological imaging. The cross-fertilisation between medical image analysis, medical imaging physics and technology, and domain knowledge from medicine and biology has spurred a truly interdisciplinary effort that stretched outside the original boundaries of the disciplines that gave birth to this field and created stimulating and enriching synergies.

Precision Imaging is not a new discipline but rather a distinct emphasis in medical imaging borne at the crossroads between and unifying the efforts behind mechanistic and phenomenological model-based imaging. Precision Imaging is characterised by being descriptive, predictive and integrative. It captures three main directions in the effort to deal with the information deluge in imaging sciences and thus achieve wisdom from data, information, and knowledge. Precision imaging can lead to carefully and mechanistically engineered imaging biomarkers and the use of medical imaging-based computational modelling and simulation for improved regulatory science and innovation of medical products.

This talk summarises and formalises our vision of Precision Imaging for Precision Medicine, and highlights some connections with past research and our current focus on large-scale computational phenomics and in silico clinical trials.

We have three other speakers at this event.

• Bart ter Haar Romenij (TU/e), our society president will give his farewell lecture on something quite special: understanding deep learning from first principles and the human visual system. To support the credibility: all will be explained with life coding.

• Efstratios Gavves(UvA), will teach us on the so crucial (and rewarding) dynamics of temporal machine learning.

• Veronika Cheplygina(IT Univ. of Copenhagen), also leaving the board, has an exciting lecture about shortcomings of machine learning and recommendations to overcome these.

May 2022
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