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11 March 2020 Deep learning-based computational histology staining using spatial light interference microscopy (SLIM) Data (Conference Presentation)
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Proceedings Volume 11249, Quantitative Phase Imaging VI; 1124913 (2020) https://doi.org/10.1117/12.2550335
Event: SPIE BiOS, 2020, San Francisco, California, United States
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.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael J. Fanous, Hassaan Majeed, Yuchen He, Nahil Sobh, and Gabriel Popescu "Deep learning-based computational histology staining using spatial light interference microscopy (SLIM) Data (Conference Presentation)", Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124913 (11 March 2020); https://doi.org/10.1117/12.2550335
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