Advanced Computational Methods for Oncological Image Analysis

Leonardo Rundo; Carmelo Militello; Vincenzo Conti; Fulvio Zaccagna; Changhee Han

doi:10.3390/jimaging7110237

Advanced Computational Methods for Oncological Image Analysis

J Imaging. 2021 Nov 12;7(11):237. doi: 10.3390/jimaging7110237.

Authors

Leonardo Rundo^{1

2}, Carmelo Militello³, Vincenzo Conti⁴, Fulvio Zaccagna^{5

6}, Changhee Han⁷

Affiliations

¹ Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK.
² Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy.
³ Institute of Molecular Bioimaging and Physiology, Italian National Research Council (IBFM-CNR), 90015 Cefalù, Italy.
⁴ Faculty of Engineering and Architecture, University of Enna KORE, 94100 Enna, Italy.
⁵ Department of Biomedical and Neuromotor Sciences, University of Bologna, 40138 Bologna, Italy.
⁶ IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, 40139 Bologna, Italy.
⁷ Saitama Prefectural University, Saitama 343-8540, Japan.

Abstract

The Special Issue "Advanced Computational Methods for Oncological Image Analysis", published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...].

Publication types

Editorial