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Proceedings Paper

Deep learning-based computational histology staining using spatial light interference microscopy (SLIM) Data (Conference Presentation)
Author(s): Michael J. Fanous; Hassaan Majeed; Yuchen He; Nahil Sobh; Gabriel Popescu

Paper Abstract

Histological staining of tissue samples is one of the most helpful tools in diagnosing and prognosing various cancers. However, in order to prepare the slide for a histopathologist to examine, the tissue must first undergo a series of time-consuming processes, such as a staining technique to visually differentiate features in the sample. In this study, we use a label-free method to generate a virtually-stained microscopic image using a single spatial light interference microscopy (SLIM) image of an unlabeled tissue sample, therefore eliminating the need for standard histochemical administration. This novel approach will render histopathological practices faster and more cost-effective, while providing medically relevant dry mass information associated with SLIM images.

Paper Details

Date Published: 11 March 2020
Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124913 (11 March 2020); doi: 10.1117/12.2550335
Show Author Affiliations
Michael J. Fanous, Beckman Institute for Advanced Science and Technology (United States)
Hassaan Majeed, Univ. of Illinois (United States)
Yuchen He, Univ. of Illinois (United States)
Nahil Sobh, Univ. of Illinois (United States)
Gabriel Popescu, Univ. of Illinois (United States)

Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)

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