Liu Y, Wang X, Xu F, Li D, Yang H, Sun N, Fan YC, Yang X. Risk factors of chronic kidney disease in chronic hepatitis b:a hospital-based case- control study from china. J Clin Transl Hepatol. 2022;10(2):238–46.

Article  Google Scholar 

Egger J, Gsxaner C, Pepe A, Li J. Medical deep learning – a systematic meta-review. Comput Methods and Prog Biomed. 2020;221:106874.

Article  Google Scholar 

Castera L. Noninvasive methods to assess liver disease in patients with hepatitis b or c. Gastroenterology. 2012;142(6):1293–302.

Article  Google Scholar 

Crossan C, Tsochatzis EA, Longworth L, Gurusamy K, Davidson B, Rodríguez-Perálvarez M, Mantzoukis K, O’Brien J, Thalassinos E, Papastergiou V, Burroughs A. Cost-effectiveness of non-invasive methods for assessment and monitor- ing of liver fibrosis and cirrhosis in patients with chronic liver disease: systematic review and economic evaluation. Health Technol Assess. 2015;19(9):1–410.

Article  Google Scholar 

Kluwer, W.: Current opinion in gastroenterology. Curr Opin Gastroenterol. 2012;28(6):547–550.

Kremer S, Lersy F, De Sèze J, Ferré JC, Maamar A, Carsin-Nicol B, Collange O, Bonneville F, Adam G, Martin-Blondel G, Rafiq M. Brain mri findings in severe covid-19: a retrospective observational study. Radiology. 2020;297(2):E242–51.

Article  Google Scholar 

Kandemirli SG, Dogan L, Sarikaya ZT, Kara S, Kocer N. Brain mri find- ings in patients in the intensive care unit with covid-19 infection. Radiology. 2020;297(1): 201697.

Article  Google Scholar 

Woods JC, Wild JM, Wielpütz MO, Clancy JP, Hatabu H, Kauczor H-U, van Beek EJR, Altes TA. Current state of the art mri for the longitudinal assessment of cystic fibrosis. J Magnet Res Imag. 2019;52(5):1306–20.

Article  Google Scholar 

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Comput Sci. 2014. https://doi.org/10.48550/arXiv.1409.1556.

Article  Google Scholar 

Tan M, Le QV (2021) Efficientnetv2: Smaller models and faster training. International conference on machine learning, 2021;10096–10106.

Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architec- tures of deep convolutional neural networks. Artif Intell Rev. 2019;53:5455–516.

Article  Google Scholar 

Gore JC. Artificial intelligence in medical imaging. Magnet Res Imag. 2019;68:A1–4.

Google Scholar 

Debelee TG, Schwenker F, Ibenthal A, Yohannes D. Survey of deep learning in breast cancer image analysis. Evol Syst. 2020;11(1):143–63.

Article  Google Scholar 

Gupta S, Gupta M (2021) Deep learning for brain tumor segmentation using magnetic resonance images. In: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

Lockard JS, Wyler AR. The influence of attending on seizure activity in epileptic monkeys. Epilepsia. 2010;20(2):157–68.

Article  Google Scholar 

Huang S, Lee F, Miao R, Si Q, Chen Q. A deep convolutional neural network architecture for interstitial lung disease pattern classification. Med Biol Eng Comput. 2020;58(5):725–37.

Article  Google Scholar 

Xiang K, Jiang B, Shang D. The overview of the deep learning integrated into the medical imaging of liver: a review. Hepatol Int. 2021;15:868–80.

Article  Google Scholar 

Mahesh B.: Machine learning algorithms-a review. (IJSR). 2020;9(1):381–386.

Chan H, Hadjiiski LM, Samala RK. Computer-aided diagnosis in the era of deep learning. Med Phys. 2020. https://doi.org/10.1002/mp.13764.

Article  Google Scholar 

Islam MM, Wu CC, Poly TN, Nguyen PAA, Li YCJ. Prediction of fatty liver disease using machine learning algorithms. Comput Methods Prog Biomed. 2019;170:23–9.

Article  Google Scholar 

Li W, Huang Y, Zhuang BW, Liu GJ, Hu HT, Li X, Liang JY, Wang Z, Huang XW, Zhang, C.Q.a. Multiparametric ultrasomics of significant liver fibrosis: a machine learning-based analysis. Eur Radiol. 2019;29(3):1496–506.

Article  Google Scholar 

Ayeldeen H, Shaker O, Ayeldeen G, Anwar KM (2016) Prediction of liver fibrosis stages by machine learning model: A decision tree approach. In: Third World Conference on Complex Systems, IEEE

House MJ, Bangma SJ, Thomas M, Gan EK, Ayonrinde OT, Adams LA, Olynyk JK, Pierre TGS. Texture-based classification of liver fibrosis using mri. J Magnet Res Imag. 2013;41(2):322–8.

Article  Google Scholar 

Barry B, Buch K, Soto JA, Jara H, Anderson SW. Quantifying liver fibro- sis through the application of texture analysis to diffusion weighted imaging. Magn Reson Imag. 2013;32(1):84–90.

Article  Google Scholar 

Sheth D, Giger ML. Artificial intelligence in the interpretation of breast cancer on mri. J Magnet Res Imag. 2019;51(5):1310–24.

Article  Google Scholar 

Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Deep learning for staging liver fibrosis on ct: a pilot study. Eur Radiol. 2018;28(11):4578–85.

Article  Google Scholar 

Chen M, Zhang B, Topatana W, Cao J, Cai X. Classification and mutation prediction based on histopathology he images in liver cancer using deep learning. npj Precis Oncol. 2020;4(1):14.

Article  Google Scholar 

Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;2818–2826.

Zhen SH, Cheng M, Tao YB, Wang YF, Cai XJ. Deep learning for accu- rate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Front Oncol. 2020. https://doi.org/10.3389/fonc.2020.00680.

Article  Google Scholar 

Das B, Toraman S. Deep transfer learning for automated liver cancer gene recognition using spectrogram images of digitized dna sequences. Biomed Signal Process Control. 2022;72:103317.

Article  Google Scholar 

Rao Y, Zhao W, Zhu Z, Lu J, Zhou J. Global filter networks for image classification. Adv Neural Inform Process Syst. 2021;34:980–93.

Google Scholar 

Ding X, Zhang X, Zhou Y, Han J, Ding G, Sun J (2022) Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. arXiv e-prints

Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: Attention over convolution kernels. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020;11030–11039.

Zhu Z, Xu, M., Bai, S., Huang, T., Bai, X., Zhu, Z.: Asymmetric Non-local Neural Networks for Semantic Segmentation. Proceedings of the IEEE/CVF international conference on computer vision. 2019;593–602.

Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., Zhang, L.: Cvt: Introducing convolutions to vision transformers. Proceedings of the IEEE/CVF international conference on computer vision. 2021

Srinivas, A., Lin, T.Y., Parmar, N., Shlens, J., Vaswani, A.: Bottleneck trans- formers for visual recognition. Proceedings of the IEEE/CVF international conference on computer vision and pattern recognition. 2021;16519–16529

Ott, M., Edunov, S., Grangier, D., Auli, M.: Scaling neural machine translation. International conference on machine learning. 2018;3956–3965.

Wen Y, Zhang K, Li Z, Qiao Y. A discriminative feature learning approach for deep face recognition. In: Leibe B, Matas J, Sebe N, Welling M, editors. Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII. Cham: Springer; 2016.

Google Scholar 

Ranjan R, Castillo CD, Chellappa R (2017) L2-constrained softmax loss for discriminative face verification. arXiv preprint arXiv:1703.09507.

Wang F, Xiang X, Cheng J, Yuille AL (2017) Normface: l2 hypersphere embedding for face verification. arXiv

Deng, J., Guo, J., Zafeiriou, S.: Arcface: Additive angular margin loss for deep face recognition. 2019;4690–4699.

Van Horn, G., Cole, E., Beery, S., Wilber, K., Belongie, S., Mac Aodha, O.: Benchmarking representation learning for natural world image collections. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021;12884–12893

Wang H, Wang Y, Zhou Z, Ji X, Gong D, Zhou J, Li Z, Liu W (2018) Cos- face: Large margin cosine loss for deep face recognition. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Wang F, Cheng J, Liu W, Liu H. Additive margin softmax for face verification. IEEE Signal Process Lett. 2018;25(7):926–30.

Article  Google Scholar 

Rao Y, Zhao W, Tang Y, Zhou J, Lim S-N, Lu J (2022) HorNet: Effi- cient high-order spatial interactions with recursive gated convolutions (2022) arXiv:2207.14284 [cs.CV]

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv

Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localiza- tion. In: IEEE International Conference on Computer Vision (2017)

Liu W, Wen Y, Yu Z, Yang M (2016) Large-margin softmax loss for convolutional neural networks. JMLR.org

Lin TY, Goyal P, Girshick R, He K, Dollar P (2017) Focal loss for dense object detection. IEEE

Meng, Q., Zhao, S., Huang, Z., Zhou, F.: Magface: A universal representation for face recognition and quality assessment. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2021;14225–14234.

Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, Ferrell LD, Liu Y-C, Torbenson MS, Unalp-Arida A, Yeh M, McCullough AJ, Sanyal AJ. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 2005;41(6):1313–21.

Article  Google Scholar 

Niu S, Liu Y, Wang J, Song H. A decade survey of transfer learning. IEEE Trans Artif Intell. 2020;1(2):151–66.

Article  Google Scholar 

Deng J.: A large-scale hierarchical image database. Proc. of IEEE Computer Vision and Pattern Recognition. 2009

Sompong C, Wongthanavasu S (2014) Mri brain tumor segmentation using glcm cellular automata-based texture feature. In: Computer Science Engineering Conference. 192–197

Saihood A, Karshenas H, Nilchi ARN. Deep fusion of gray level co- occurrence matrices for lung nodule classification. PLoS ONE. 2022;17(9):e0274516.

Article  Google Scholar 

Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101.

Davies WS. Digital image processing methods. Optics and Lasers in Eng. 1994;4:250–1.

Article  Google Scholar