Florian Jug

Florian Jug

  • Group Leader, Jug Group
  • Head of Image Analysis Facility, Image Analysis Facility

Dr. Florian Jug holds a PhD in Computational Neuroscience from the Institute of Theoretical Computer Science at ETH Zurich. His research aims at pushing the boundary of what AI and machine learning can do to better analyze and quantify biological data. At HT, Dr. Jug covers the full breadth of bio-image computing, from research on novel methods for computer vision and machine learning, all the way to offering bio-image analysis as a service.

Florian Jug is a strong proponent of open access science, open AI and ML methods, and open source software. His team is a core contributor to  Fiji (~100,000 active users)  and collaboratively develops open methods such as CARENoise2VoidPN2VDivNoising, etc. He organizes scientific conferences (e.g the I2K conference), workshops (e.g. the BIC workshops at top-tier computer vision conferences) and various practical courses on machine learning for bio-image analysis (e.g. DL@MBL in Woods Hole) or microscopy (e.g. Quantitative Imaging at Cold Spring Harbor).



  • 12/2020 - Journal of Cell Biology

    3D FIB-SEM reconstruction of microtubule–organelle interaction in whole primary mouse β cells

    This collaborative work is a good example for how members of our team can collaborate with biologists and technologists to improve the quantitative analysis work required to gain insights into essential processes related to human health and pathology, in this particular case into the subcellular organization of insulin producing β cells. Microtubules play a major […]

  • 11/2020 - Protein Science

    The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis

    For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open‐source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative […]

  • 08/2020 - arXiv

    Improving Blind Spot Denoising for Microscopy

    Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on […]

  • 08/2020 - BioRxiv

    ZeroCostDL4Mic: an open platform to use Deep-Learning in Microscopy

    The resources and expertise needed to use Deep Learning (DL) in bioimaging remain significant barriers for most laboratories. We present https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki, a platform simplifying access to DL by exploiting the free, cloud-based computational resources of Google Colab. https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki allows researchers to train, evaluate, and apply key DL networks to perform tasks including segmentation, detection, denoising, restoration, resolution enhancement […]

  • 06/2020 - arXiv

    DivNoising: Diversity Denoising with Fully Convolutional Variational Autoencoders

    Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. But there are limitations to what can be restored in corrupted images, and any given method […]