This lab is part of our journey through computational imaging and modeling techniques, and the use of AI in biomedical applications. It is designed to give you a comprehensive understanding of how computational imaging is transforming society in general and biomedicine in particular and the role it will play in the future of biomedical research.
update: 2025-01-24
If you have a subscription to ChatGPT Plus, you can also try out the the Medical AI Assistant (UiBmed - ELMED219 & BMED365) GPT and see if you can get it to answer some of your questions.
| Notebook | 1-Click Notebook |
|---|---|
| 00-test-installation.ipynb Test your installation |
 |
| 01-imaging-intro.ipynb Illustration of basic concepts and methods in imaging |
|
| 02-mri-intro.ipynb Introduction to Magnetic Resonance Imaging |
|
| 03-imc-intro.ipynb Introduction to Imaging Mass Cytometry |
|
Spend some time playing around with the provided examples. You'll find some questions for you to investigate in the notebooks. If you're already familiar with medical imaging and image analysis you can try your hand at more advanced examples, or, even better, help out other less experienced team members.
(in the order of duration ...)
-
Uncovering Cellular Networks by Imaging Mass Cytometry by Bernd Bodenmiller, University of Zurich & ETH Zurich [link] (27:45 min)
- see also the Bodenmiller lab and their GitHub repo (https://github.com/BodenmillerGroup)
-
The future of computational imaging by Gordon Wetzstein, Stanford University Computational Imaging Lab [link] (35:48 min)
- see also Stanford University School of Engineering: The Future of Everything podcast
(in the order of most recent ...)
-
Ma J et al. Segment anything in medical images (article published online 22 Jan 2024) Nature Communications 2024;15:654 [link] CC-BY-4.0. Their GitHub repo [MedSAM] (SAM = Segment Anything Model)
-
Tian D et al. The role of large language models in medical image processing: a narrative review (article published online 3 Jan 2024) Quant Imaging Med Surg 2024;14(1):1108-1121 [link] CC-BY-4.0
-
Hu M et al. Advancing medical imaging with language models: A journey from n-grams to ChatGPT ( preprint published online 11 Apr 20234) ArXiv 2023, /abs/2304.04920 [link]
-
Srivastav S et al. ChatGPT in radiology: The advantages and limitations of artificial intelligence for medical imaging diagnosis (published online Jul 6 2023) Cureus 2023; 15(7): e41435 [link] CC-BY-4.0
-
Pavel Iakubovskii: Segmentation models [link]
(a very comprehensive Python library with Neural Networks for Image Segmentation based on PyTorch) -
Yoni Chechik: AI_is_Math [link] - frequently updated; MIT license; see also his https://www.aiismath.com and AlgoMonkeys
(a place to learn a wide range of computer vision and deep learning algorithms + the math behind them, including class notes and interactive notebooks)- Also listed in Image processing notebooks [link] - a list of 145 public repositories matching "Jupyter and Image processing" using algorithms to make computers analyze the content of digital images.
- 152 Visual Phenomena & Optical Illusions with explanations (by Michael Bach) [link]