Intro to Machine Learning-DeepLearning-DeepimageJ - [NEUBIASAcademy@Home] Webinar

Introduction to Machine Learning (ML) and Deep Learning (DL) for Bioimage Analysis:

4:34
– Basic concepts to understand the workflow of ML/DL methods.

45:34
– Technical requirements and key aspects to apply ML/DL to bioimage data analysis.

55:17
– Some important tools and resources to get started with these techniques, with a special focus in DeepImageJ: a user-friendly plugin to process images using pre-trained DL models in ImageJ/Fiji.

 Learning outcomes: After this session you will be able to:
– Understand the fundamentals behind machine learning and deep learning.
– Design a basic deep learning solution for your bioimage problem.
– Share your deep learning model with the bioimage community using ImageJ

Recommended prior knowledge:
– Basic knowledge of statistics and linear algebra.
– Basic knowledge of ImageJ / Fiji processing.

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Authors and Speaker:

Ignacio Arganda-Carreras is an Ikerbasque Research Fellow at the Computer Science and Artificial Intelligence Department at University of the Basque Country (UPV/EHU). Ignacio is expert trainer in Bioimage Analysis and developer of numerous open source software tools (e.g trainable WEKA segmentation, …).
The webinar was live-moderated by: Daniel Sage, senior researcher of the Biomedical Imaging Group at EPFL, specialist in open-source imaging software for a wide range of applications in microscopy (e.g. super-resolution microscopy, deconvolution, image restoration), Carlos García López de Haro and Estibaliz Gómez de Mariscal, both at the Biomedical and Instrumentation Group at Univ. Carlos III, Madrid. Carlos, Estibaliz, and Daniel are among the main developers of DeepImageJ.

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