Jason T. Smith
Rensselaer Polytechnic Institute
49 Papers
81 Citations
Jason T. Smith is an academic researcher from Rensselaer Polytechnic Institute. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 6, co-authored 26 publications. Previous affiliations of Jason T. Smith include Southern Connecticut State University.
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Papers
In vitro and in vivo optimization of infrared laser treatment for injured peripheral nerves
Juanita J. Anders,Helina Moges,Xingjia Wu,Isaac D. Erbele,Stephanie L. Alberico,Edward K. Saidu,Jason T. Smith,Brian A. Pryor +7 more
TL;DR: The objective of this study was to demonstrate that for a selected wavelength effective in vitro dosing parameters could be translated to effective in vivo parameters.
Deep Learning in Biomedical Optics
Lei Tian,Brady Hunt,Muyinatu A. Lediju Bell,Ji Yi,Jason T. Smith,Marien Ochoa,Xavier Intes,Nicholas J. Durr +7 more
TL;DR: In this article, a review of deep learning applications in biomedical optics with a particular emphasis on image formation is presented, including microscopy, fluorescence lifetime imaging, wide field endoscopy, optical coherence tomography, photoacoustic imaging, and functional optical brain imaging.
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Distinguishing metastatic triple‐negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy
Ethan Bendau,Jason T. Smith,Lin Zhang,Ellen Ackerstaff,Natalia Kruchevsky,Binlin Wu,Jason A. Koutcher,Robert R. Alfano,Lingyan Shi +8 more
TL;DR: This study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.
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UNMIX-ME: spectral and lifetime fluorescence unmixing via deep learning.
TL;DR: This work presents "UNMIX-ME" (unmix multiple emissions), a deep learning-based fluorescence unmixing routine, capable of quantitative fluorophore un Mixing by simultaneously using both spectral and temporal signatures.
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Macroscopic fluorescence lifetime topography enhanced via spatial frequency domain imaging.
TL;DR: The results demonstrate that the presented computational approach can retrieve the depth of fluorescence inclusions, especially when coupled with optical properties estimation, with high accuracy.
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