AI Platform for Learning 3D Cellular Objects in High-Resolution Holo-Tomographic Volumes
Goals
- Develop an AI-framework with deep learning algorithms for learning 3D high-resolution cellular and subcellular details within holo-tomographic volumes
- Benchmark both predictive and computational performance of developed algorithms
- Based on “Best Software Practices” industrialize most promising AI-algorithm including graphical user interface (GUI) and System Tests
- Familiarize with holo-tomographic microscopy – including practical experiments
- Explore current Machine Learning (ML) and Deep Learning (DL) algorithms to learn subcellular 3D structures in holo-tomographic volumes
- Setup AI-framework (workflow) with functionality:
- Load and preprocess holo-tomographic data (provided by Nanolive)
- Train-validate-test
- Visualize and store
- Implement Prototype including:
- The currently existing ML-algorithms as starting point (base line)
- The most promising ML – / DL-algorithm evaluated above
- The possible extension of the initial ground truth data
- Benchmark and iteratively improve implemented ML- / DL-algorithms
- Industrialize most promising ML- / DL-algorithm using best Software Practices including:
- Test Driven Development
- Modularity
- Extensibility: e.g. new features via on-line improving labelling quality
- Ergonomic Graphical User Interfaces (GUI)
- Document and present Master Thesis
- Final document
- One-Minute Video Summary
- 30 minute presentation
- Machine/Deep Learning and Software Engineering
- Python, TensorFlow, mxNet
- Optional: scikit-learn, ImageJ, MATLAB
- Choi, W. et al. Tomographic phase microscopy. Nat. Methods 4, 717-719 (2007).
- Cotte, Y. et al. Marker-free phase nanoscopy. Nat. Photonics 7, 113-117 (2013).
- Chen, L,C. et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs IEEE Transactions on Pattern Analysis and Machine Intelligence
Kind of Work
40% Research, 40% Software Development, 20% Benchmarking
Requirements
Machine Learning & Deep Learning Basics, CV, Python, Matlab
Time & Effort
Master’s Thesis, 1 Person
Contact
fabian.schenkel@scs.ch


