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Novel computer-aided systems for interpreting immunohistochemistry (IHC) results in breast cancer based on deep learning algorithms: A systematic review
Sasan Salehi Nezamabadi, Haniyeh Rafiepoor, Mohammad Amin Barati, Elham Angouraj Taghavi, Golnar Khorsand, Parsa Mirzayi, Ali Taheri, Behzad Amanpour-Gharaei, Saman Asadi, Seyed-Ali Sadegh-Zadeh, Saeid Amanpour https://bccr.tums.ac.ir/index.php/bccrj/article/view/506 Breast cancer is a prevalent disease worldwide and the accurate diagnosis and prognosis of breast cancer are essential for the development of effective treatment plans. Pathology remains the gold […]
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Comparing Texture Analysis of Apparent Diffusion Coefficient MRI in Hepatocellular Adenoma and Hepatocellular Carcinoma
Ayoob Dinar Abdullah, Behzad Amanpour-Gharaei, Mohssen Nassiri Toosi, Sina Delazar, Hamidraza Saligheh Rad, and Arvin Arian https://doi.org/10.7759/cureus.51443 Aim: This study aimed to assess the effectiveness of using MRI-apparent diffusion coefficient (ADC) map-driven radiomics to differentiate between hepatocellular adenoma (HCA) and hepatocellular carcinoma (HCC) features. Materials and methods: The study involved 55 patients with liver tumors […]
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COVID-19 risk factors using machine learning
Banoei, M. M., Rafiepoor, H., Zendehdel, K., Seyyedsalehi, M. S., Nahvijou, A., Allameh, F., & Amanpour, S. (2023). Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study. Frontiers in medicine, 10, 1170331.
آخرین پست ها
- Novel computer-aided systems for interpreting immunohistochemistry (IHC) results in breast cancer based on deep learning algorithms: A systematic review
- Comparing Texture Analysis of Apparent Diffusion Coefficient MRI in Hepatocellular Adenoma and Hepatocellular Carcinoma
- COVID-19 risk factors using machine learning