Keywords
How to Cite
Abstract
Ushbu maqolada alkogolsiz yog‘li jigar to‘qimalarini neyron tarmoqlari yordamida virtual gistokimyoviy bo‘yashning nazariy asoslari, ishlash prinsiplari va diagnostikadagi ahamiyati tahlil qilindi. Tadqiqot davomida konvolyutsion neyron tarmoqlari (CNN), U-Net, Pix2Pix hamda CycleGAN arxitekturalarining raqamli patologiyadagi imkoniyatlari ilmiy adabiyotlar asosida o‘rganildi. Virtual bo‘yash texnologiyasining laboratoriya xarajatlarini kamaytirishi, diagnostika tezligini oshirishi va patolog shifokor faoliyatini qo‘llab-quvvatlashi ilmiy jihatdan asoslandi.
References
1. Eslam M., Sanyal A.J., George J. et al. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology. 2020.
2. Younossi Z.M., Golabi P., Paik J.M. et al. Global Epidemiology of NAFLD. Nature Reviews Gastroenterology & Hepatology. 2023.
3. Rinella M.E., Lazarus J.V., Ratziu V. et al. Clinical Practice Guidance for MASLD. Journal of Hepatology. 2024.
4. Ronneberger O., Fischer P., Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI. 2015.
5. Isola P., Zhu J.Y., Zhou T., Efros A. Image-to-Image Translation with Conditional Adversarial Networks. CVPR. 2017.
6. He K., Zhang X., Ren S., Sun J. Deep Residual Learning for Image Recognition. CVPR. 2016.