[1] Ouyang X, Karanam S, Wu Z Y et al. Learning hierarchical attention for weakly-supervised chest X-ray abnormality localization and diagnosis[J]. IEEE Transactions on Medical Imaging, 40, 2698-2710(2021).

[2] Chen B Z, Zhang Z, Lin J Y et al. Two-stream collaborative network for multi-label chest X-ray Image classification with lung segmentation[J]. Pattern Recognition Letters, 135, 221-227(2020).

[3] Qiu Y F, Zhang J X, Lan H et al. Improved ResNet image classification model based on tensor synthesis attention[J]. Laser & Optoelectronics Progress, 60, 0610008(2023).

[4] Anthimopoulos M, Christodoulidis S, Ebner L et al. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network[J]. IEEE Transactions on Medical Imaging, 35, 1207-1216(2016).

[5] Kumar P, Grewal M, Srivastava M M. Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs[M]. Campilho A, Karray F, ter Haar R B. Image analysis and recognition. Lecture notes in computer science, 10882, 546-552(2018).

[6] Zhao Q Y, Kong P, Min J Z et al. A review of deep learning methods for the detection and classification of pulmonary nodules[J]. Journal of Biomedical Engineering, 36, 1060-1068(2019).

[7] Gilanie G, Attique M, Naweed S et al. Object extraction from T2 weighted brain MR image using histogram based gradient calculation[J]. Pattern Recognition Letters, 34, 1356-1363(2013).

[8] Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks[J]. IEEE Transactions on Medical Imaging, 35, 2369-2380(2016).

[9] Rajinikanth V, Satapathy S C, Fernandes S L et al. Entropy based segmentation of tumor from brain MR images-a study with teaching learning based optimization[J]. Pattern Recognition Letters, 94, 87-95(2017).

[10] Bülow H, Dooley L, Wermser D. Application of principal axes for registration of NMR image sequences[J]. Pattern Recognition Letters, 21, 329-336(2000).

[11] Zhao W D, Liu J D, Wang M M et al. Fast image registration method based on improved AKAZE algorithm[J]. Laser & Optoelectronics Progress, 60, 0610007(2023).

[12] Liao R, Miao S, de Tournemire P et al. An artificial agent for robust image registration[C], 4168-4175(2017).

[13] Wang X S, Peng Y F, Lu L et al. ChestX-Ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases[C], 3462-3471(2017).

[14] Guan Q J, Huang Y P, Luo Y W et al. Discriminative feature learning for thorax disease classification in chest X-ray images[J]. IEEE Transactions on Image Processing, 30, 2476-2487(2021).

[15] Ma Y B, Zhou Q H, Chen X S et al. Multi-attention network for thoracic disease classification and localization[C], 1378-1382(2019).

[16] Guan Q J, Huang Y P. Multi-label chest X-ray image classification via category-wise residual attention learning[J]. Pattern Recognition Letters, 130, 259-266(2020).

[17] Wang H Y, Wang S S, Qin Z B et al. Triple attention learning for classification of 14 thoracic diseases using chest radiography[J]. Medical Image Analysis, 67, 101846(2021).

[18] Tan H, Dong S J. Pixel-level concrete crack segmentation using pyramidal residual network with omni-dimensional dynamic convolution[J]. Processes, 11, 546(2023).

[19] Li J Y, Chen M H, Yang R J et al. Study on fundus image screening of diabetic retinopathy[J]. Chinese Journal of Lasers, 49, 1107001(2022).

[20] Pan X R, Ge C J, Lu R et al. On the integration of self-attention and convolution[C], 805-815(2022).

[21] Zhou T, Ye X Y, Liu F J et al. Aided diagnosis of pneumonia radiographs based on the lightweight direction Transformer model[J]. Acta Optica Sinica, 43, 1310001(2023).

[22] Ryou S, Jeong S G, Perona P. Anchor loss: modulating loss scale based on prediction difficulty[C], 5991-6000(2020).

[23] Zhang Z R, Li Q, Guan X. Multilabel chest X-ray disease classification based on a dense squeeze-and-excitation network[J]. Journal of Image and Graphics, 25, 2238-2248(2020).

[24] Shao L Y, Li Q, Guan X et al. Efficient channel attention-based disease classification algorithm for chest radiographs[J]. Laser & Optoelectronics Progress, 60, 1217001(2023).

[25] Irvin J, Rajpurkar P, Ko M et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 590-597(2019).