DL@MBL: Deep Learning For Microscopy Image Analysis - AI Summary

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: (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 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.