HER2GAN: Overcome the Scarcity of HER2 Breast Cancer Dataset Based on Transfer Learning and GAN Model.

Autor: Mirimoghaddam MM; Radiation Sciences Research Center (RSRC), Aja University of Medical Sciences, Tehran, Iran., Majidpour J; Department of Computer Science, University of Raparin, Rania, Iraq. Electronic address: jafar.majidpoor@uor.edu.krd., Pashaei F; Radiation Sciences Research Center (RSRC), Aja University of Medical Sciences, Tehran, Iran. Electronic address: Fakhereh.pashaee@gmail.com., Arabalibeik H; Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran., Samizadeh E; Department of Pathology, School of Medicine and Imam Reza Hospital, AJA University of Medical Sciences, Tehran, Iran., Roshan NM; Pathology Department, Mashhad University of Medical Sciences, Mashhad, Iran., Rashid TA; Computer Science and Engineering Department, University of Kurdistan Hewlêr, Erbil, Iraq.
Jazyk: angličtina
Zdroj: Clinical breast cancer [Clin Breast Cancer] 2024 Jan; Vol. 24 (1), pp. 53-64. Date of Electronic Publication: 2023 Sep 29.
DOI: 10.1016/j.clbc.2023.09.014
Abstrakt: Introduction: Immunohistochemistry (IHC) is crucial for breast cancer diagnosis, classification, and individualized treatment. IHC is used to measure the levels of expression of hormone receptors (estrogen and progesterone receptors), human epidermal growth factor receptor 2 (HER2), and other biomarkers, which are used to make treatment decisions and predict how well a patient will do. The evaluation of the breast cancer score on IHC slides, taking into account structural and morphological features as well as a scarcity of relevant data, is one of the most important issues in the IHC debate. Several recent studies have utilized machine learning and deep learning techniques to resolve these issues.
Materials and Methods: This paper introduces a new approach for addressing the issue based on supervised deep learning. A GAN-based model is proposed for generating high-quality HER2 images and identifying and classifying HER2 levels. Using transfer learning methodologies, the original and generated images were evaluated.
Results and Conclusion: All of the models have been trained and evaluated using publicly accessible and private data sets, respectively. The InceptionV3 and InceptionResNetV2 models achieved a high accuracy of 93% with the combined generated and original images used for training and testing, demonstrating the exceptional quality of the details in the synthesized images.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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