[1] Zhou T, Ye X Y, Lu H L et al. Dense convolutional network and its application in medical image analysis[EB/OL]. https://doi.org/10.1155/2022/2384830

[2] Naralasetti V, Shaik R K, Katepalli G et al. Deep learning models for pneumonia identification and classification based on X-ray images[J]. Traitement Du Signal, 38, 903-909(2021).

[3] Gong X, Chen Z L, Wu L et al. Transfer learning based mixture of experts classification model for high-resolution remote sensing scene classification[J]. Acta Optica Sinica, 41, 2301003(2021).

[4] Jain R, Nagrath P, Kataria G et al. Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning[J]. Measurement, 165, 108046(2020).

[5] Li H, Zeng N Y, Wu P S et al. Cov-Net: a computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision[J]. Expert Systems With Applications, 207, 118029(2022).

[6] Li X, He M, Luo H B. Occluded pedestrian detection algorithm based on improved YOLOv3[J]. Acta Optica Sinica, 42, 1415003(2022).

[7] Naseer M, Ranasinghe K, Khan S et al. Intriguing properties of vision transformers[EB/OL]. https://arxiv.org/abs/2105.10497

[8] Okolo G I, Katsigiannis S, Ramzan N. IEViT: an enhanced vision transformer architecture for chest X-ray image classification[J]. Computer Methods and Programs in Biomedicine, 226, 107141(2022).

[9] Park S, Kim G, Oh Y et al. Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification[J]. Medical Image Analysis, 75, 102299(2022).

[10] Yuan L, Chen Y P, Wang T et al. Tokens-to-token ViT: training vision transformers from scratch on ImageNet[C], 538-547(2022).

[11] Liang S, Nie R C, Cao J D et al. FCF: feature complement fusion network for detecting COVID-19 through CT scan images[J]. Applied Soft Computing, 125, 109111(2022).

[12] Lin Z S, Wang Y Y D, Wang H et al. Expansion of depth-of-field of scattering imaging based on DenseNet[J]. Acta Optica Sinica, 42, 0436001(2022).

[13] Chen Y P, Dai X Y, Chen D D et al. Mobile-former: bridging MobileNet and transformer[C], 5260-5269(2022).

[14] Mehta S, Rastegari M. MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer[EB/OL]. https://arxiv.org/abs/2110.02178

[15] Kim Y, Ohn I, Kim D. Fast convergence rates of deep neural networks for classification[J]. Neural Networks, 138, 179-197(2021).

[16] Wang Y S, Ma X J, Chen Z Y et al. Symmetric cross entropy for robust learning with noisy labels[C], 322-330(2020).

[17] Kermany D S, Zhang K, Goldbaum M. Labeled optical coherence tomography (OCT) and chest X-ray images for classification[J]. Mendeley data, 2, 17632(2018).

[18] Giełczyk A, Marciniak A, Tarczewska M et al. Pre-processing methods in chest X-ray image classification[J]. PLoS One, 17, e0265949(2022).

[19] Cui Y, Jia M L, Lin T Y et al. Class-balanced loss based on effective number of samples[C], 9260-9269(2020).

[20] El-Nouby A, Touvron H, Caron M et al. XCiT: cross-covariance image transformers[EB/OL]. https://arxiv.org/abs/2106.09681

[21] Maaz M, Shaker A, Cholakkal H et al. EdgeNeXt: efficiently amalgamated CNN-transformer architecture for mobile vision applications[M]. Karlinsky L, Michaeli T, Nishino K. Computer vision-ECCV 2022 workshops. Lecture notes in computer science, 13807, 3-20(2023).

[22] Li J S, Xia X, Li W et al. Next-ViT: next generation vision transformer for efficient deployment in realistic industrial scenarios[EB/OL]. https://arxiv.org/abs/2207.05501

[23] Chetoui M, Akhloufi M A. Explainable vision transformers and radiomics for COVID-19 detection in chest X-rays[J]. Journal of Clinical Medicine, 11, 3013(2022).

[24] Balasubramanian K, Ananthamoorthy N P, Ramya K. An end-end deep learning framework for lung infection recognition using attention-based features and cross average pooling[J]. International Journal for Multiscale Computational Engineering, 20, 67-82(2022).