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Title: In the use of Artificial Intelligence and Hyperspectral Imaging in Digital Pathology for Breast Cancer Cell Identification
Authors: Quintana Quintana, Laura 
Ortega Sarmiento, Samuel 
León Martín, Sonia Raquel 
Fabelo Gómez, Himar Antonio 
Balea Fernandez, Francisco Javier 
Sauras, Esther
Lejeune, Marylene
Bosch, Ramón
Lopez, Carlos
Marrero Callicó, Gustavo Iván 
Editors: Levenson, Richard M.
Tomaszewski, John E.
Ward, Aaron D.
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
Keywords: Hyperspectral microscopy
Digital pathology
Artificial intelligence
Breast cancer
Issue Date: 2022
Publisher: SPIE-int Soc Optical Engineering
Project: Talent Imágenes Hiperespectrales Para Aplicaciones de Inteligencia Artificial 
Journal: Proceedings of SPIE - The International Society for Optical Engineering 
Conference: Medical Imaging 2022: Digital and Computational Pathology 
Abstract: Hyperspectral (HS) imaging (HSI) is a novel technique that allows a better understanding of materials, being an improvement respect to other imaging modalities in multiple applications. Specifically, HSI technology applied to breast cancer histology, could significantly reduce the time of tumor diagnosis at the histopathology department. First, histological samples from twelve different breast cancer patients have been prepared and examined. Second, they were digitally scanned, using RGB (Red-Green-Blue) whole-slide imaging, and further annotated at cell level. Then, the annotated regions were captured with an HS microscopic acquisition system at 20× magnification, covering the 400-1000 nm spectral range. The HS data was registered (through synthetic RGB images) to the whole-slide images, allowing the transfer of accurate annotations made by pathologists to the HS image and extract each annotated cell from such image. Then, both spectral and spatial-spectral classifications were carried out to automatically detect tumor cells from the rest of the coexisting cells in the breast tissue (fibroblasts and lymphocytes). In this work, different supervised classifiers have been employed, namely kNN (k-Nearest-Neighbors), Random Forest, DNN (Deep Neural Network), Support Vector Machines (SVM) and CNN (Convolutional Neural Network). Test results for tumor cells vs. fibroblast classification show that the kNN performed with the best sensitivity/specificity (64/52%) trade-off and the CNN achieved the best sensitivity and AUC results (96% and 0.91, respectively). Moreover, at the tumor cells vs. lymphocyte classification, kNN also provided the best sensitivity-specificity ratio (58.47/58.86%) and an F1-score of 74.12%. The SVM algorithm also provided a high F-score result (70.38%). In conclusion, several machine learning algorithms provide promising results for cell classification in breast cancer tissue and so, future work must include these discoveries for faster cancer diagnosis.
ISBN: 9781510649538
ISSN: 0277-786X
DOI: 10.1117/12.2611419
Source: Proceedings of SPIE - The International Society for Optical Engineering [ISSN 0277-786X], v. 12039, 120390E, (2022)
Appears in Collections:Actas de congresos
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