DL@MBL: Deep Learning for Microscopy Image Analysis

The goal of this course is to familiarize researchers in the life sciences with state-of-the-art deep learning techniques for microscopy image analysis and to introduce them to tools and frameworks that facilitate independent application of the learned material after the course.

Course date:
Aug 26, 2022 - Sep 06, 2022
Application due date:
Jun 02, 2022

Directors: Jan Funke, HHMI Janelia Research Campus; Anna Kreshuk, EMBL Heidelberg; Florian Jug, Fondazione Human Technopole

Course Description

The goal of this course is to familiarize researchers in the life sciences with state-of-the-art deep learning techniques for microscopy image analysis and to introduce them to tools and frameworks that facilitate independent application of the learned material after the course.

The following topics will be covered extensively during lectures, exercises, and project work:

  1. image denoising and restoration (fully supervised and self-supervised),
  2. image translation (i.e., generating fluorescent-like images from label-free acquisitions),
  3. image segmentation (various flavors will be presented and explored),
  4. image classification,
  5. object detection and tracking in 2D and 3D videos, and
  6. discrete optimization techniques (e.g. for tracking or for the reconstruction of biological structures of interest).

The course will be organized into two phases of five days each: (1) A lecture- and exercise-based phase that introduces basic concepts of deep learning and allows participants to learn about state-of-the-art methods and tools. (2) A project-based phase, where students will work together with numerous TAs to apply the newly acquired skills to their own datasets. Faculty and TAs will assist the students in data preparation, problem formalization, network architecture design, tool selection, model training, prediction, reconstruction, and evaluation.

Students will leave the course with an appreciation for the power and limitations of deep learning as well as broad knowledge of key tools that are needed in order to apply deep-learning methods to microscopy data.

The course assumes at least a basic familiarity with Python programming, although it does not assume any prior experience with machine learning techniques. Students are encouraged to bring their own microscopy datasets to work on. This course is limited to a maximum of 24 students.