Bootcamp Course on Deep Learning for Microscopy Image Analysis
A small, hands-on bootcamp course to familiarize life science researchers with state-of-the-art deep learning techniques for microscopy image analysis and introduce tools and frameworks that enable independent application of the material.
Topics
The following will be covered extensively during lectures, exercises, and project work:
- Image denoising and restoration (fully supervised and self-supervised)
- Image translation (e.g., virtually staining biological structures in label-free images)
- Image segmentation (various approaches will be presented and explored)
- Image classification
- Object detection and tracking in 2D and 3D time-lapse movies
- Explainability methods
- Popular models (e.g., U-Nets, GANs, Transformers)
Format
The bootcamp will be organized into two one-week phases. Participants are expected to stay for the duration.
Week 1 — Lectures & Exercises: from basic to advanced core deep-learning concepts with deep dives into established methods and tools to demonstrate in detail how existing state-of-the-art methods and tools operate.
Week 2 — Project Work: work with experts to apply concepts from Week 1 to your own datasets and analysis problems. Faculty and TAs will assist with data preparation, problem formalization, network architecture design, tool selection, model training, prediction, and evaluation.
Participants will leave the bootcamp with an appreciation for the power and limitations of deep learning and broad knowledge of key tools for applying deep-learning methods to microscopy image data.
Prerequisites and Materials
- Python fluency required; no prior machine learning experience needed
- Pre-course materials provided to help you assess your programming skills with image data in Python
- Bring your own microscopy datasets (or your lab's) for Week 2
Fees + Expenses | There are no registration fees, and Janelia covers the cost of meals and onsite accommodations. Travel expenses are not covered, but support is available for those in need.
Attendance | Participants must commit to staying for the duration of the course.
Schedule | The course will begin at 6 pm ET on June 4 and end after dinner (~8 pm) on June 18, with departures the following morning.
Application Instructions
To be considered, applicants must apply online and answer questions about motivation for taking the course, the data you will bring, and prior experience with Python and machine learning.
Application deadline: Jan 15, 2026 (11:59 pm ET)
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Application deadline: Jan 15, 2026 (11:59pm ET)