14-17 April 2020
Center of Systems Biology Dresden
Europe/Berlin timezone

Registration deadline: 28 February 2020


Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. While the image formation process in electron microscopy is quite different, similar limitations apply also there.

In this course we will introduce all students to recently developed software packages that make heavy use of deep learning to enables biological observations beyond the physical limitations of microscopes.

The number of participants is limited to 12.  The course is open for DIPP PhD students and CBG Postdocs.

We will inform you after 28 February 2020 whether a slot in the programming course could be assigned to you!

Center of Systems Biology Dresden
Seminar Room Top Floor (3rd floor)
Pfotenhauerstr. 108 01307 Dresden

LECTURERS: Florian Jug (CSBD) and Pavel Tomancak (MPI-CBG)


  • Microscopy: How to acquire suitable data for CARE and Friends?
  • CARE: Content-aware image restoration via python packages from CSBDeep.
  • CARE trained Noise2Noise: training restoration models without having ground-truth images available.
  • Noise2Void: training denoising models on any microscopy data.
  • Fiji basics: some basic Fiji tricks and how to apply a trained restoration network on you data directly in Fiji.


This course will be very hands-on, with some time spent at the microscope in order to acquire suitable data, and lots of time in front of your computer to familiarize yourself with CARE and Noise2Void.

In the mornings, we will present the exercises for the day and present all needed concepts. The remainder of the day you can interact with the other course members and instructors in order to find your preferred solution(s).

The day will usually end with short presentations by selected participants, showing off their way to solve the tasks at hand. On the evening of the third day small groups of students will give a final presentation.

BACKGROUND READING: This is an introductory course that does not assume prior knowledge. PhD Students or postdocs with prior experience will be given more challenging exercises. If you cannot wait, here is a nice online Python tutorial:






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