Summary of Three-dimensional, Multimodal Synchrotron Data For Machine Learning Applications, by Calum Green et al.
Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications
by Calum Green, Sharif Ahmed, Shashidhara Marathe, Liam Perera, Alberto Leonardi, Killian Gmyrek, Daniele Dini, James Le Houx
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a unique dataset for developing advanced deep learning and data fusion pipelines in medical and physical sciences. The dataset is based on a zinc-doped Zeolite 13X sample, which was characterized using multi-resolution micro X-ray computed tomography and spatially resolved X-ray diffraction computed tomography. The data is multimodal, three-dimensional, and multispectral, making it suitable for various machine learning techniques, such as super-resolution, multimodal data fusion, and 3D reconstruction algorithm development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper provides a dataset for developing advanced machine learning techniques in medical and physical sciences. It’s a special kind of material called zinc-doped Zeolite 13X that was studied using different imaging methods. The result is a unique dataset that can be used to train machines to do things like see very small details, combine different types of data, and create detailed 3D images. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Super resolution
