Hossein Maleki
Georgia Institute of Technology
6 Papers
13 Citations
Hossein Maleki is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Inverse problem & Disease. The author has an hindex of 2, co-authored 5 publications.
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Papers
•Posted Content
Manifold Learning for Knowledge Discovery and Intelligent Inverse Design of Photonic Nanostructures: Breaking the Geometric Complexity.
Zhongjian Mou,Mohammadreza Zandehshahvar,Yashar Kiarashi,Muliang Zhu,Hossein Maleki,Tyler Brown,Ali Adibi +6 more
TL;DR: In this paper, a manifold learning approach based on manifold learning for knowledge discovery and inverse design with minimal complexity in photonic nanostructures is presented. But it is limited to pre-selected and usually over-complex structures.
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COVID-19 pneumonia chest radiographic severity score: Variability assessment among experienced and In-training radiologists and creation of a Multi-reader composite score database for artificial intelligence algorithm development.
Marly van Assen,Mohammadreza Zandehshahvar,Hossein Maleki,Yashar Kiarashi,Timothy Arleo,Arthur E. Stillman,Peter D. Filev,Amir H. Davarpanah,Eugene Berkowitz,Stefan Tigges,Scott J. Lee,Brianna L. Vey,Ali Adibi,Carlo N. De Cecco +13 more
TL;DR: Experienced and in-training radiologists demonstrate good inter and intra observer agreement in COVID-19 pneumonia severity classification and create a multi reader database suitable for AI development.
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Fano Resonant All-Dielectric Metasurfaces for Polarization-Sensitive Structural Coloration
Omid Hemmatyar,Zhou Lu,Tyler Brown,Hossein Maleki,Ali Adibi +4 more
- 10 May 2020
TL;DR: In this article, the authors demonstrate Fano resonant all-dielectric metasurfaces comprising of high and median-index nanopillars (HfO 2, TiO 2 and ZrO 2 ) with zero loss in visible range for polarization-sensitive structural coloration.
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Manifold Learning for Reducing the Design Complexity of Photonic Nanostructures
Mohammadreza Zandehshahvar,Yashar Kiarashi,Muliang Zhu,Hossein Maleki,Tyler Brown,Ali Adibi +5 more
- 09 May 2021
TL;DR: A new manifold-learning-based approach to reduce the geometric complexity of the inverse design of photonic nanostructures and it is shown how this approach can provide valuable insight about the underlying physics of their operation.
Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease.
Mohammadreza Zandehshahvar,Marly van Assen,Hossein Maleki,Yashar Kiarashi,Carlo N. De Cecco,Ali Adibi +5 more
TL;DR: In this article, a two-stage transfer learning technique was used to train a convolutional neural network (CNN) to classify four classes of disease severity (normal, mild, moderate, and severe) with the average Area Under the Curve (AUC) of 0.93.