• Acta Optica Sinica
  • Vol. 40, Issue 18, 1810003 (2020)
Hong Huang1、*, Chao Peng1, Ruoyu Wu1, Junli Tao2, and Jiuquan Zhang2
Author Affiliations
  • 1Key Laboratory of Optoelectronic Technology & Systems, Ministry of Education, Chongqing University, Chongqing 400044, China;
  • 2Department of Radiology, Chongqing University Cancer Hospital, Chongqing 400030, China
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    DOI: 10.3788/AOS202040.1810003 Cite this Article Set citation alerts
    Hong Huang, Chao Peng, Ruoyu Wu, Junli Tao, Jiuquan Zhang. Self-Supervised Transfer Learning of Pulmonary Nodule Classification Based on Partially Annotated CT Images[J]. Acta Optica Sinica, 2020, 40(18): 1810003 Copy Citation Text show less
    References

    [1] Chen W Q, Zheng R S, Baade P D et al. Cancer statistics in China, 2015[J]. CA: a Cancer Journal for Clinicians, 66, 115-132(1900).

    [2] Jacobs C, van Rikxoort E M, Scholten E T et al. Solid, part-solid, or non-solid: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system[J]. Investigative Radiology, 50, 168-173(2015).

    [3] Feng Y, Yi B S, Wu C Y et al. Pulmonary nodule recognition based on three-dimensional convolution neural network[J]. Acta Optica Sinica, 39, 0615006(2019).

    [4] 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).

    [5] Miao G, Li C F. Detection of pulmonary nodules CT images combined with two-dimensional and three-dimensional convolution neural networks[J]. Laser & Optoelectronics Progress, 55, 051006(2018).

    [6] Setio A A A, Jacobs C, Gelderblom J et al. Automatic detection of large pulmonary solid nodules in thoracic CT images[J]. Medical Physics, 42, 5642-5653(2015).

    [7] Chen C H, Chang C K, Tu C Y et al. Radiomic features analysis in computed tomography images of lung nodule classification[J]. PLoS One, 13, e0192002(2018).

    [8] Dhara A K, Mukhopadhyay S, Dutta A et al. A combination of shape and texture features for classification of pulmonary nodules in lung CT images[J]. Journal of Digital Imaging, 29, 466-475(2016).

    [9] Anwar S M, Majid M, Qayyum A et al. Medical image analysis using convolutional neural networks: a review[J]. Journal of Medical Systems, 42, 1-13(2018).

    [10] Nibali A, He Z, Wollersheim D. Pulmonary nodule classification with deep residual networks[J]. International Journal of Computer Assisted Radiology and Surgery, 12, 1799-1808(2017).

    [11] Polat H, Mehr H D. Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture[J]. Applied Sciences, 9, 940(2019).

    [12] Tajbakhsh N, Shin J Y, Gurudu S R et al[J]. Convolutional neural networks for medical image analysis: fine tuning or full training? IEEE Transactions on Medical Imaging, 35, 1299-1312.

    [13] Huang S, Li F F, Chen Q. Computed tomography image classification algorithm based on improved deep residual network[J]. Acta Optica Sinica, 40, 0310002(2020).

    [14] Chen L, Bentley P, Mori K et al. Self-supervised learning for medical image analysis using image context restoration[J]. Medical Image Analysis, 58, 101539(2019).

    [15] Zhou Z W, Sodha V. Rahman Siddiquee M M, et al. Models genesis: generic autodidactic models for 3D medical image analysis[M]. ∥Sheng D G, Liu T M, Peters T M, et al. Medical image computing and computer assisted intervention-MICCAI 2019. Cham: Springer, 11767, 384-393(2019).

    Hong Huang, Chao Peng, Ruoyu Wu, Junli Tao, Jiuquan Zhang. Self-Supervised Transfer Learning of Pulmonary Nodule Classification Based on Partially Annotated CT Images[J]. Acta Optica Sinica, 2020, 40(18): 1810003
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