Tissue characterization using a multi-pixel sensor system and partial least squares regression
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Woods, CM; Senlik, O; Jokerst, NM
Published in: Optics Infobase Conference Papers
January 1, 2020
Diffuse reflectance spectroscopy systems often exhibit low SNR, reducing tissue characterization accuracy. We present a new analysis showing lower SNR requirements when partial least squares regression is used for prediction instead of more traditional techniques.
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Published In
Optics Infobase Conference Papers
EISSN
2162-2701
Publication Date
January 1, 2020
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Woods, C. M., Senlik, O., & Jokerst, N. M. (2020). Tissue characterization using a multi-pixel sensor system and partial least squares regression. In Optics Infobase Conference Papers.
Woods, C. M., O. Senlik, and N. M. Jokerst. “Tissue characterization using a multi-pixel sensor system and partial least squares regression.” In Optics Infobase Conference Papers, 2020.
Woods CM, Senlik O, Jokerst NM. Tissue characterization using a multi-pixel sensor system and partial least squares regression. In: Optics Infobase Conference Papers. 2020.
Woods, C. M., et al. “Tissue characterization using a multi-pixel sensor system and partial least squares regression.” Optics Infobase Conference Papers, 2020.
Woods CM, Senlik O, Jokerst NM. Tissue characterization using a multi-pixel sensor system and partial least squares regression. Optics Infobase Conference Papers. 2020.
Published In
Optics Infobase Conference Papers
EISSN
2162-2701
Publication Date
January 1, 2020