Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications
| Autor: | Green, Calum, Ahmed, Sharif, Marathe, Shashidhara, Perera, Liam, Leonardi, Alberto, Gmyrek, Killian, Dini, Daniele, Houx, James Le |
|---|---|
| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Machine learning techniques are being increasingly applied in medical and physical sciences across a variety of imaging modalities; however, an important issue when developing these tools is the availability of good quality training data. Here we present a unique, multimodal synchrotron dataset of a bespoke zinc-doped Zeolite 13X sample that can be used to develop advanced deep learning and data fusion pipelines. Multi-resolution micro X-ray computed tomography was performed on a zinc-doped Zeolite 13X fragment to characterise its pores and features, before spatially resolved X-ray diffraction computed tomography was carried out to characterise the homogeneous distribution of sodium and zinc phases. Zinc absorption was controlled to create a simple, spatially isolated, two-phase material. Both raw and processed data is available as a series of Zenodo entries. Altogether we present a spatially resolved, three-dimensional, multimodal, multi-resolution dataset that can be used for the development of machine learning techniques. Such techniques include development of super-resolution, multimodal data fusion, and 3D reconstruction algorithm development. Comment: 9 pages, 4 figures. Image Processing and Artificial Intelligence Conference, 2024 |
| Databáze: | arXiv |
| Externí odkaz: |
|