Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI | http://purl.tuc.gr/dl/dias/7A180DD9-F903-4802-9312-9EAE0372DF0D | - |
| Identifier | https://doi.org/10.1109/JBHI.2020.2991043 | - |
| Identifier | https://ieeexplore.ieee.org/document/9103969 | - |
| Language | en | - |
| Extent | 21 pages | en |
| Extent | 3,42 megabytes | en |
| Title | AI in medical imaging informatics: current challenges and future directions | en |
| Creator | Panayides Andreas S. | en |
| Creator | Amini Amir | en |
| Creator | Filipovic Nenad | en |
| Creator | Sharma Ashish | en |
| Creator | Tsaftaris, Sotirios A | en |
| Creator | Young Alistair | en |
| Creator | Foran David | en |
| Creator | Do Nhan | en |
| Creator | Golemati Spyretta | en |
| Creator | Kurc Tahsin | en |
| Creator | Huang Kun | en |
| Creator | Nikita Konstantina | en |
| Creator | Veasey Ben P. | en |
| Creator | Zervakis Michail | en |
| Creator | Ζερβακης Μιχαηλ | el |
| Creator | Saltz Joel H. | en |
| Creator | Pattichis, Constantinos S | en |
| Publisher | Institute of Electrical and Electronics Engineers | en |
| Content Summary | This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2021-07-14 | - |
| Date of Publication | 2020 | - |
| Subject | Medical imaging | en |
| Subject | Image analysis | en |
| Subject | Image classification | en |
| Subject | Image processing | en |
| Subject | Image segmentation | en |
| Subject | Image visualization | en |
| Subject | Integrative analytics | en |
| Subject | Machine learning | en |
| Subject | Deep learning | en |
| Subject | Big data | en |
| Bibliographic Citation | A. S. Panayides, A. Amini, N. D. Filipovic, A. Sharma, S. A. Tsaftaris, A. Young, D. Foran, N. Do, S. Golemati, T. Kurc, K. Huang, K. S. Nikita, B. P. Veasey, M. Zervakis, J. H. Saltz, and C. S. Pattichis, “AI in medical imaging informatics: current challenges and future directions,” IEEE J. Biomed. Health Inform., vol. 24, no. 7, pp. 1837–1857, Jul. 2020. doi: 10.1109/JBHI.2020.2991043 | en |
Copyright © DIAS 2013
