Florian Jug

Florian Jug

Il dott. Florian Jug ha conseguito un dottorato in neuroscienze computazionali presso l’Institute of Theoretical Computer Science dell’ETH di Zurigo. La sua ricerca mira a superare i confini di ciò che l’intelligenza artificiale e l’apprendimento automatico possono fare per analizzare e quantificare meglio i dati biologici. In HT, il dottor Jug copre l’intera gamma del calcolo delle bio-immagini, dalla ricerca su nuovi metodi per la visione artificiale e l’apprendimento automatico, fino al servizio di analisi di bio-immagini. 

Florian Jug è un forte sostenitore della scienza open access, grazie anche alla condivisione di metodi ML e di intelligenza artificiale  e software open source. Il suo team da un forte contributo al software Fiji (circa 100.000 user attivi) e sviluppa in modo collaborativo metodi quali CARE, Noise2Void, PN2V, DivNoising ecc. Organizza conferenze scientifiche (per es. la conferenza I2K), workshop (per es. i workshop BIC a conferenze di alto livello in tema di computer vision) e vari corsi di formazione pratici sull’apprendimento automatico per l’analisi di bio immagini (per es. [email protected] a Woods Hole) o sulla microscopia (per es. Quantitative Imaging a Cold Spring Harboe).

Pubblicazioni

  • 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 […]