Follow-up: Workshop: Prof. Bart ter Haar Romeny – „Neuro-Mathematics of Deep Learning” (2019.04.04, OK Center)

On Thursday, April 4, 2019, from 4 PM to 8 PM at OK Center (1 Traian street, Bucharest), Prof. Bart ter Haar Romeny from the Eindhoven University of Technology, the Netherlands, Department of Biomedical Technology, held a lecture on „Neuro-Mathematics of Deep Learning”.

Below, photos and videos from the event (also, the audio recording).

Video recording:

See full list of videos »

Photo album on Google Photos »

Photo album on Facbeook »

(I will post the audio recording at a later time)

We also talked about

The success of deep learning neural networks is evident, leading to a revolution in the field. The breakthrough is enabled by the discovery of how to train a multi-layered neural net with backpropagation, by the advent of cheap GPU processing power and the availability of huge amounts of data.

The workshop focused on examples of important convolutional neural networks today and is organized in partnership with SSIMA Re:Imagine Healthcare (Festival of Innovation in MedTech).

We zoomed in on an important biomedical application: the large-scale screening for diabetes by automatic analysis of retinal fundus images. With the progression of diabetes, blood vessels begin to leak, and this can be detected at high resolution and at low cost in the retina. We discussed automated and quantitative biomarkers of early retinal damage to be exploited in deep learning. All algorithms are based on ‘brain-inspired computing’.

We focused on possible intrinsic mechanisms of deep learning. How does it actually work? Can we devise some mathematical modeling? We can learn a lot from modern brain research, where optical and physiological recording techniques shed new light on how the functional circuits in the brain may be computing: neuro-mathematics.

The biomedical engineer, speaking both languages, is just the right professional to benefit from and contribute to these developments.
The workshop/lecture was highly visual and is aimed at a broad audience.


  • Some knowledge of linear algebra (convolution, eigenvectors), calculus (coordinate systems, vectors) is recommended.
  • Existing crash courses by Google or Udacity are a plus.


  • Part I, Introduction to deep learning and convolutional neural nets
  • Part II, Back-propagation and face recognition, examples of trained network needed
  • Part III, A geometric, brain-inspired model for deep learning
  • Part IV, Hands-on experience with given Mathematica code

About the Trainer:
Prof. Bart ter Haar Romeny from the Eindhoven University of Technology, the Netherlands, Department of Biomedical Technology research interests focus on biologically inspired image analysis algorithms, multi-valued 3D visualization, brain connectivity by diffusion tensor imaging, computer-aided diagnosis, and image-guided neurosurgery. He initiated and leads the RetinaCheck project, a large international screening program for diabetes. He published over 250 papers (h-index: 43, > 13500 citations).

Prof. Romeny is President of the Dutch Society for Pattern Recognition and Image Processing and has been President of the Dutch Society for Biophysics & Biomedical Engineering and the Dutch Society of Clinical Physics. He is founder and organizer of SSIMA Re: Imagine Healthcare (the International Summer School of Imaging with Medical Applications).
He is reviewer for many journals, conferences and science foundations, and organized many international Summer Schools. Prof. Romeny is EMBS Distinguished Lecturer, Senior Member of IEEE, Fellow of EAMBES, Board member of IAPR, and Honorary Chair Professor at NTUST, Taiwan.

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