Ms. Esther Sauras Profile

Esther Sauras

at Hospital de Tortosa Verge de la Cinta

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Publications (2)

Proceedings Article | 7 April 2023 Presentation + Paper

YOLOX-based framework for nuclei detection on whole-slide histopathological RGB and hyperspectral images

Carlos Vega, Laura Quintana, Samuel Ortega, Himar Fabelo, Esther Sauras, Noèlia Gallardo, Daniel Mata, Marylene Lejeune, Carlos Lopez, Gustavo Callico

Proceedings Volume 12471, 124711A (2023) https://doi.org/10.1117/12.2654036

KEYWORDS: RGB color model, Tumors, Principal component analysis, Tissues, Cancer detection, Object detection, Visualization, Hyperspectral imaging, Data modeling

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Proceedings Article | 4 April 2022 Presentation + Paper

In the use of artificial intelligence and hyperspectral imaging in digital pathology for breast cancer cell identification

Laura Quintana, Samuel Ortega, Raquel Leon, Himar Fabelo, Francisco J. Balea-Fernández, Esther Sauras, Marylene Lejeune, Ramon Bosch, Carlos Lopez, Gustavo Callico

Proceedings Volume 12039, 120390E (2022) https://doi.org/10.1117/12.2611419

KEYWORDS: Tumors, Breast cancer, Breast, Pathology, Hyperspectral imaging, Databases, Cancer, Artificial intelligence

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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.

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