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Antonio Di Ieva
Researcher at Macquarie University
Publications - 157
Citations - 3489
Antonio Di Ieva is an academic researcher from Macquarie University. The author has contributed to research in topics: Medicine & Fractal analysis. The author has an hindex of 28, co-authored 130 publications receiving 2815 citations. Previous affiliations of Antonio Di Ieva include St. Michael's GAA, Sligo & Medical University of Vienna.
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Journal ArticleDOI
Dynamics of Forest Fragmentation and Connectivity Using Particle and Fractal Analysis
Ion Andronache,Marian Marin,Rico Fischer,Helmut Ahammer,Marko Radulovic,Ana-Maria Ciobotaru,Herbert F. Jelinek,Antonio Di Ieva,Radu-Daniel Pintilii,Cristian Constantin Drăghici,Grigore Vasile Herman,Alexandru-Sabin Nicula,Alexandru-Sabin Nicula,Adrian Gabriel Simion,Ioan Vlad Loghin,Daniel Constantin Diaconu,Daniel Peptenatu +16 more
TL;DR: The fractal and particle analysis provide a relevant methodological framework to further the understanding of the spatial effects of economic pressure on forestry.
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Aggressive pituitary adenomas—diagnosis and emerging treatments
TL;DR: The need to develop new biomarkers to facilitate the early detection of clinically aggressive pituitary adenomas is highlighted and emerging markers that hold promise for their identification are discussed.
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Fractals in the Neurosciences, Part I: General Principles and Basic Neurosciences
TL;DR: Fractal geometry is a mathematical model that offers a universal language for the quantitative description of neurons and glial cells as well as the brain as a whole, with its complex three-dimensional structure, in all its physiopathological spectrums.
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Fractals in the Neurosciences, Part II Clinical Applications and Future Perspectives
Antonio Di Ieva,Antonio Di Ieva,Francisco J. Esteban,Fabio Grizzi,Wlodzimierz Klonowski,Miguel Martín-Landrove +5 more
TL;DR: The main applications of fractals to the clinical neurosciences are reviewed for a holistic approach towards a fractal geometry model of the brain.
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Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning.
Sidong Liu,Zubair Shah,Zubair Shah,Aydin Sav,Carlo Russo,Shlomo Berkovsky,Yi Qian,Enrico Coiera,Antonio Di Ieva +8 more
TL;DR: The findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas’ IDH status prediction.