[1] Sung H, Ferlay J, Siegel R L et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. A Cancer Journal for Clinicians, 71, 209-249(2021).

[2] Yoshida N, Doyama H, Yano T et al. Early gastric cancer detection in high-risk patients: a multicentre randomised controlled trial on the effect of second-generation narrow band imaging[J]. Gut, 70, 67-75(2021).

[3] Liu Y M, Xiao Z Y. Automatic segmentation algorithm of liver tumor based on feature fusion[J]. Laser & Optoelectronics Progress, 58, 1417001(2021).

[4] Sun Y J, Qu Z Y, Li Y H. Study on target detection of breast tumor based on improved mask R-CNN[J]. Acta Optica Sinica, 41, 0212004(2021).

[5] Zhao X, Wang X, Wang H K. End-to-end segmentation of brain white matter hyperintensities combining attention and Inception modules[J]. Acta Optica Sinica, 41, 0910002(2021).

[6] Shichijo S, Nomura S, Aoyama K et al. Application of convolutional neural networks in the diagnosis of helicobacter pylori infection based on endoscopic images[J]. EBioMedicine, 25, 106-111(2017).

[7] Hirasawa T, Aoyama K, Tanimoto T et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images[J]. Gastric Cancer, 21, 653-660(2018).

[8] Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks[J]. Digestive Endoscopy: Official Journal of the Japan Gastroenterological Endoscopy Society, 31, e34-e35(2019).

[9] Wu L L, Zhou W, Wan X Y et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots[J]. Endoscopy, 51, 522-531(2019).

[10] Zhang Y Q, Li F X, Yuan F Q et al. Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence[J]. Digestive and Liver Disease, 52, 566-572(2020).

[11] Tang D H, Zhou J, Wang L et al. A novel model based on deep convolutional neural network improves diagnostic accuracy of intramucosal gastric cancer (with video)[J]. Frontiers in Oncology, 11, 622827(2021).

[12] Cho B J, Bang C S, Park S W et al. Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network[J]. Endoscopy, 51, 1121-1129(2019).

[13] Li L, Chen Y S, Shen Z et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging[J]. Gastric Cancer, 23, 126-132(2020).

[14] Luo H Y, Xu G L, Li C F et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study[J]. The Lancet Oncology, 20, 1645-1654(2019).

[15] Kolesnikov A, Beyer L, Zhai X H et al. Big Transfer (BiT): general visual representation learning[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020, 12350, 491-507(2020).

[16] Jung A B, Wada K, Crall J et al. Imgaug[EB/OL]. https://github.com/aleju/imgaug#citation

[17] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).

[18] Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer learning[J]. Journal of Big Data, 3, 9(2016).

[19] He K M, Zhang X Y, Ren S Q et al. Identity mappings in deep residual networks[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016, 9908, 630-645(2016).

[20] Russakovsky O, Deng J, Su H et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 115, 211-252(2015).

[21] Deng J, Dong W, Socher R et al. ImageNet: a large-scale hierarchical image database[C], 248-255(2009).

[22] Sun C, Shrivastava A, Singh S et al. Revisiting unreasonable effectiveness of data in deep learning era[C], 843-852(2017).

[23] Google. TensorFlow Hub of BiT[EB/OL]. https://tfhub.dev/google/collections/bit/1

[24] Zhang H Y, Cissé M, Dauphin Y N et al. Mixup: beyond empirical risk minimization[C](2018).

[25] Goyal P, Dollár P, Girshick R et al. Accurate, large minibatch SGD: training ImageNet in 1 hour[EB/OL]. https://arxiv.org/abs/1706.02677

[26] Keskar N S, Mudigere D, Nocedal J et al. On large-batch training for deep learning: generalization gap and sharp minima[C](2017).

[27] Kandel I, Castelli M. The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset[J]. ICT Express, 6, 312-315(2020).