СУНЪИЙ ИНТЕЛЛЕКТДАН ФОЙДАЛАНИБ СИНТЕТИК РАҚАМЛИ ТИББИЙ ТАСВИРЛАРНИ ҲОСИЛ ҚИЛИШ ВА БАҲОЛАШ

Mualliflar

  • Баходир Мўминов Муҳаммад ал Хоразмий номиндаги Тошкент ахборот технологиялари университети
  • Нигорахон Насимова Муҳаммад ал Хоразмий номиндаги Тошкент ахборот технологиялари университети
  • Рашид Насимов Тошкент давлат иқтисодиёт университети

DOI:

https://doi.org/10.55439/EIT/vol10_iss3/a34

Kalit so‘zlar:

эхокардиограмма, сунъий интеллект, GAN тармоғи, FID қиймати, синтетик тиббий тасвир, кўрсатгичлар бўйича баҳолаш

Abstrak

Сўнгги йилларда тиббиёт соҳасида қарор қабул қилишга кўмаклашувчи интеллектуал дастурларни ишлаб чиқишга бўлган қизиқишлар ортиб бормоқда. Касалликларга ташхис қўйиш, таснифлаш, тавсиялар бериш жараёнларининг аниқлигини юқори бўлиши катта ҳажмдаги маълумотлар тўпламини ҳосил қилиш билан чамбарчас боғлиқдир. Ушбу мақолада FID қийматини тезкор ҳисоблашда фойдаланилган усуллардан фарқли тиббий тасвирларни баҳолашда аниқлиги FID қийматидан юқори бўлган FMD қийматини киритишни таклиф этдилди.

Author Biographies

Баходир Мўминов , Муҳаммад ал Хоразмий номиндаги Тошкент ахборот технологиялари университети

кафедра мудири, т.ф.д

 

Нигорахон Насимова , Муҳаммад ал Хоразмий номиндаги Тошкент ахборот технологиялари университети

докторант

 

Рашид Насимов , Тошкент давлат иқтисодиёт университети

Рақамли иқтисодиёт ва ахборот технологиялари” кафедраси доценти (PhD)

 

Bibliografik manbalar

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Onur Karaog‘lu, Hasan Yeћakir Bilge, Ihsan Uluer, “Removal of speckle noises from ultrasound images using five different deep learning networks”, Engineering Science and Technology, an International Journal, Volume 29, 2022, 101030, ISSN 2215-0986, https://doi.org/10.1016/j.jestch.2021.06.010. 3 Liu, R., Zhou, S., Guo, Y. et al. U2F-GAN: Weakly Supervised Super-pixel Segmentation in Thyroid Ultrasound Images. Cogn Comput 13, 1099–1113 (2021).

Negi, Anuja & Joseph Raj, Alex Noel & Nersisson, Ruban & Zhuang, Zhemin & M, Murugappan. RDA-UNET-WGAN: An Accurate Breast Ultrasound Lesion Segmentation Using Wasserstein Generative Adversarial Networks. Arabian Journal for Science and Engineering. (2020).

Peng, B., Huang, X., Wang, S., & Jiang, J. (2019). A Real-Time Medical Ultrasound Simulator Based on a Generative Adversarial Network Model. Proceedings. International Conference on Image Processing, 2019, 4629–4633.

Teng, Long & Fu, ZhLiang & Yao, Yu. (2020). Interactive Translation in Echocardiography Training System With Enhanced Cycle-GAN. IEEE Access. PP. 1-1.

Zhang, Qingjie & Zhao, Junjuan & Long, Xiangmeng & Luo, Quanyong & Wang, Ren & Ding, Xuehai & Shen, Chentian. (2022). AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network. Diagnostics. 12. 253. 10.3390/diagnostics12020253.

Abdi, A.H., Tsang, T.S., & Abolmaesumi, P. (2019). GAN-enhanced Conditional Echocardiogram Generation. ArXiv, abs/1911.02121.

Singh N.K., Raza K. (2021) Medical Image Generation Using Generative Adversarial Networks: A Review. In: Patgiri R., Biswas A., Roy P. (eds) Health Informatics: A Computational Perspective in Healthcare. Studies in Computational Intelligence, vol 932. Springer, Singapore.

Sugawara, Yusuke & Shiota, Sayaka & Kiya, Hitoshi. (2019). Checkerboard artifacts free convolutional neural networks. APSIPA Transactions on Signal and Information Processing. 8. 10.1017/ATSIP.2019.2.

Wojna, Z., Ferrari, V., Guadarr, S., Silberman, N., Chen, L. C., Fathi, A., & Uijlings, J. (2019). The devil is in the decoder: Classification, regression and gans. International Journal of Computer Vision, 127(11), 1694-1706.

Bargsten, Lennart & Schlaefer, Alexander. (2020). SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing. International Journal of Computer Assisted Radiology and Surgery. 15. 10.1007/s11548-020-02203-1.

N. Nasimova, B. Muminov, R. Nasimov, K. Abdurashidova and M. Abdullaev, "Comparative Analysis of the Results of Algorithms for Dilated Cardiomyopathy and Hypertrophic Cardiomyopathy Using Deep Learning," 2021 International Conference on Information Science and Communications Technologies (ICISCT), 2021, pp. 1-5.

Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved for training gans. Advances in neural information processing systems, 29.

Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, 6629–6640.

Alexander Mathiasen, Frederik Hvilshoj, “Backpropagating through Fré Inception Distance”, arXiv:2009.14075v2 [cs.LG] 14 Apr 2021.

Lin, T.-Y. and Maji, S. Improvedilinear Pooling with CNNs. In British Machine Vision Conference (BMVC), 2017.

FVD: A NEW METRIC FOR VIDEO GENERATION.

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2022-06-30

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Мўминов , Б., Насимова , Н., & Насимов , Р. (2022). СУНЪИЙ ИНТЕЛЛЕКТДАН ФОЙДАЛАНИБ СИНТЕТИК РАҚАМЛИ ТИББИЙ ТАСВИРЛАРНИ ҲОСИЛ ҚИЛИШ ВА БАҲОЛАШ. Economics and Innovative Technologies, 10(3), 324–338. https://doi.org/10.55439/EIT/vol10_iss3/a34

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Миллий иқтисодиёт тармоқ ва соҳаларида ахборот-коммуникация технологияларини қўллаш