@inproceedings{a1b6ae73f3514674a15b2126aea0552f,
title = "An automatic whole-slide hyperspectral imaging microscope",
abstract = "Whole slide imaging (WSI) is a common step used in histopathology to quickly digitize stained histological slides. Digital whole-slide images not only improve the efficiency of labeling but also open the door for computer-aided diagnosis, specifically machine learning-based methods. Hyperspectral imaging (HSI) is an imaging modality that captures data in various wavelengths, some beyond the range of visible lights. In this study, we developed and implemented an automated microscopy system that can acquire hyperspectral whole slide images (HWSI). The system is robust since it consists of parts that can be swapped and bought from different manufacturers. We used the automated system and built a database of 49 HWSI of thyroid cancer. The automatic whole-slide hyperspectral imaging microscope can have many potential applications in biological and medical areas.",
keywords = "Hyperspectral imaging, automation, database, deep learning, whole-slide imaging",
author = "Tran, {Minh Ha} and Ofelia Gomez and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE; Label-free Biomedical Imaging and Sensing (LBIS) 2023 ; Conference date: 28-01-2023 Through 31-01-2023",
year = "2023",
doi = "10.1117/12.2650815",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Shaked, {Natan T.} and Oliver Hayden",
booktitle = "Label-free Biomedical Imaging and Sensing (LBIS) 2023",
}