Abstract
Nodule detection in chest X-ray (CXR) images is important for early screening of lung cancer. It typically requires a large number of well-annotated data to train an effective nodule detector. However, high-quality annotations are hard to obtain due to the difficulty of locating nodules in CXR images and high cost of recruiting experienced radiologists. To address this issue, we propose an inpainting-based data augmentation (DA) framework, which consists of Nodule Synthesis stage and Nodule Selection stage, to synthesize CXR images with plausible nodules for facilitating the subsequent task of nodule detection. A partial convolutional U-Net is applied in Nodule Synthesis stage, which can offer flexibility to generate nodules at various locations in lungs. Since not all the synthesized CXR images are effective for data augmentation, we introduce Nodule Selection stage to identify efficacious nodules from the synthesized CXR images, to effectively augment the variety of training data for nodule detection. Our experimental results show that our DA framework can produce synthesized CXR images with plausible nodules of high quality, whereas the data augmentation can significantly improve the nodule detection performance.
† Z. Shen and X. Ouyang—Contributed equally to this work.
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References
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Canc. J. Clin. 68(6), 394–424 (2018)
Shah, P.K., Austin, J.H., et al.: Missed non–small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect. Radiology 226(1), 235–241 (2003)
Sim, Y., Chung, M.J., Kotter, E., et al.: Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology 294(1), 199–209 (2020)
Li, X., Shen, L., Xie, X., et. al.: Multi-resolution convolutional networks for chest x-ray radiograph based lung nodule detection. Artif. Intell. Med. 103, 101744 (2019)
Nam, J.G., Park, S., Hwang, E.J., et al.: Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology 290(1), 218–228 (2019)
Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.J., Fei-Fei, L.: Thoracic disease identification and localization with limited supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8290–8299 (2018)
Liu, J., Zhao, G., Fei, Y., Zhang, M., Wang, Y., Yu, Y.: Align, attend and locate: Chest x-ray diagnosis via contrast induced attention network with limited supervision. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 10632–10641 (2019)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)
Salehinejad, H., Colak, E., et. al.: Synthesizing chest x-ray pathology for training deep con-volutional neural networks. IEEE Tran. Med. Imaging 38(5), 1197–1206 (2018)
Lin, T.Y., Goyal, P., Girshick, R., et. al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Austin, J.H., Romney, B.M., Goldsmith, L.S.: Missed bronchogenic carcinoma: radio-graphic findings in 27 patients with a potentially resectable lesion evident in retrospect. Radiology 182(1), 115–122 (1992)
Giger, M.L.: Image feature analysis and computer‐aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med. Phys. 15(2), 158–166 (1988)
Xu, X., Doi, K., Kobayashi, T., et. al.: Development of an improved cad scheme for automated detection of lung nodules in digital chest images. Med. Phys. 24(9), 1395–1403 (1997)
Chen, S., Suzuki, K.: Computerized detection of lung nodules by means of virtual dual-energy radiography. IEEE Trans. Biomed. Eng. 60(2), 369–378 (2013)
Li, X., Shen, L., Luo, S.: A solitary feature-based lung nodule detection approach for chest X-ray radiographs. IEEE J. Biomed. Health Inform. 22(2), 516–524 (2017)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Zhu, J.-Y., Park, T.P., Isola, T., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Gündel, S., Setio, A.A.A., Grbic, S., Maier, A., Comaniciu, D.: Extracting and leveraging nodule features with lung inpainting for local feature augmentation. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds.) MLMI 2020. LNCS, vol. 12436, pp. 504–512. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59861-7_51
Liu, G., Reda, F.A., Shih, K.J., Wang, T.-C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 89–105. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_6
J´egou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 11–19 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Deng, J., Dong W, Socher R, et al: Imagenet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
Settles, B.: Active learning literature survey. University of Wisconsin-Madison Department of Computer Sciences, Tech. Rep., Santa Cruz (2009)
He, K., Zhang, X., et. al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Laurens, V.D.M., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2605), 2579–2605 (2008)
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Shen, Z. et al. (2021). Nodule Synthesis and Selection for Augmenting Chest X-ray Nodule Detection. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_45
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