IEEE IEEE 14th IEEE EMBS-SPS International Summer School on Biomedical Imaging
Saint-Jacut de la Mer, Emerald Coast, Brittany
France, 19-25 June, 2022
Carlos Garcia Lopez de Haro

Carlos Garcia Lopez de Haro

Carlos Garcia Lopez de Haro is a software developer and researcher working at Institute Pasteur on the development of DeepIcy. Carlos completed his Biomedical Engineering degree at Universidad Carlos III de Madrid in 2018 and started focusing on the application of the virtues of Deep Learning to Bioimage Analysis. Carlos participated in the DeepImageJ project, that tries to make pre-trained Deep Learning models available in ImageJ, with the role of developer of the plugin. Now Carlos is developing DeepIcy to keep improving and extending the connection between Bioimage Analysis and Deep Learning.


User friendly tools for deep-learning bioimage analysis: Hands-on DeepIcy (Part II)

Carlos Garcia Lopez de Haro
Institut Pasteur, France

Summary of the course

Machine learning (ML), and in particular, Deep Neural Networks (DNN), have become an inflection point in many areas of scientific research. In the case of biomedical image analysis, these new techniques provide significant improvements in most of the tasks such as denoising, super-resolution, segmentation, detection, tracking, response prediction or computer-aided diagnosis.

Nonetheless, the use of DNN models requires previous programming knowledge, making its use a challenging task for non-expert users. This problem limits the impact of these revolutionary methods. However, some softwares are starting to build a bridge between the developers of DNN and their target users. In order to contribute to this effort DeepIcy appeared. In this practical session we are going to talk about DeepIcy and DeepImageJ, new plugins to run pre- trained Deep Learning models in Icy.

The plugin is developed in a user-friendly manner that allows running models with few clicks and no code, thus combining DNN with other powerful Bioimage Analysis routines already available in Icy. DeepIcy also integrates the BioImage.io model zoo directly into its interface, making a growing collection of Deep Learning models readily available.

During the practical session we will explore all the mentioned features of the plugin in addition to other novel ones, such as using a server as the backend of the model inference, which improves the speed and performance of the plugin greatly.

Part II

Main topic: deepIcy + Pre- and post-processing (JavaLib) + bioengine + examples (Hela + Startdist + Cellpose) + conclusion on the importance of the data

Reference papers:

Bioimage Model Zoo: https://www.biorxiv.org/content/10.1101/2022.06.07.495102v1


version 4.6, jun., 2022