[1] Ruuskanen O, Lahti E, Jennings L C et al. Viral pneumonia[EB/OL]. Lancet(2011). http://biblioteca.asmn.re.it/allegati/lancet2011viralpneumonia_120114093922.pdf
[2] Sekuboyina A, Oñoro-Rubio D, Kleesiek J et al. A relational-learning perspective to multi-label chest X-ray classification[EB/OL]. https://arxiv.org/abs/2103.06220
[3] Liu R Y, Liu L B. Detection of pulmonary nodules based on improved full convolution network model[J]. Laser & Optoelectronics Progress, 57, 161015(2020).
[4] Gao D C, Nie S D. Method for identifying benign and malignant pulmonary nodules combing deep convolutional neural network and hand-crafted features[J]. Acta Optica Sinica, 40, 2410002(2020).
[5] Rubin J, Sanghavi D, Zhao C et al. Large scale automated reading of frontal and lateral chest X-rays using dual convolutional neural networks[EB/OL]. https://arxiv.org/abs/1804.07839v2
[6] Jing B Y, Xie P T, Xing E. On the automatic generation of medical imaging reports[EB/OL]. https://arxiv.org/abs/1711.08195
[7] Pan Y L, Wang H Q, Lu Y. The application of computer aided diagnosis with artificial intelligence in medical imaging[J]. International Journal of Medical Radiology, 42, 3-7(2019).
[8] Jiang X H, Li Z. Skin lesion segmentation based on U-shaped structure context encoding and decoding network[J]. Laser & Optoelectronics Progress, 58, 1210006(2021).
[9] Rathi T. Variable weights neural network for diabetes classification[EB/OL]. https://arxiv.org/abs/2102.12984v1
[10] Ruan H Y, Chen Z L, Cheng Y S et al. Detection of pulmonary nodules based on C-3D deformable convolutional neural network model[J]. Laser & Optoelectronics Progress, 57, 041013(2020).
[11] Park S, Hwang W, Jung K H. Integrating reinforcement learning to self training for pulmonary nodule segmentation in chest X-rays[EB/OL]. https://arxiv.org/abs/1811.08840v1
[12] Syeda-Mahmood T, Wong K, Gur Y et al. Chest X-ray report generation through fine-grained label learning[M]. Martel A L, Abolmaesumi P, Stoyanov D, et al. Medical image computing and computer assisted intervention-MICCAI 2020. Lecture notes in computer science, 12262, 561-571(2020).
[13] Kumar P, Grewal M, Srivastava M M. Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs[M]. Campilho A, Karray F, Romeny B T H. International conference image analysis and recognition. Lecture notes in computer science, 10882, 546-552(2018).
[14] Rajpurkar P, Irvin J, Zhu K et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning[EB/OL]. https://arxiv.org/abs/1711.05225
[15] Zhang C M. X-ray diagnosis of common chest lesions based on deep learning method[D](2020).
[16] Wang X S, Xu Z Y, Yang D et al. Learning image labels on-the-fly for training robust classification models[EB/OL]. https://arxiv.org/abs/2009.10325v1
[17] Lenga M, Schulz H, Saalbach A. Continual learning for domain adaptation in chest X-ray classification[C], 413-423(2020).
[18] Ning G H, He Z H. Dual path networks for multi-person human pose estimation[EB/OL]. https://arxiv.org/abs/1710.10192
[19] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).
[20] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[21] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).
[22] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).