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: 2024-02-05
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 |
|---|---|
| 1-comp-imag.ipynb Exploration of digital images (micro & macro), image processing, and computational imaging (eg. Gabor filtering) |
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(in the order of duration ...)
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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)
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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 ...)
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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)
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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
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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]
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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
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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]