Abstract
Expensive and time-consuming medical imaging annotation is one of the big challenges for the deep learning-based computer-aided diagnosis (CAD) on the low-dose computed tomography (CT). To address this problem, we propose a novel active learning approach to improve the training efficiency for a deep network-based lung nodule detection framework as well as reduce the annotation cost. The informative CT scans, such as the samples that inconspicuous or likely to produce high false positives, are selected and further annotated for the nodule detector network training. A simple yet effective schema suggests the samples by ranking the uncertainty loss predicted by multi-layer feature maps and the Region of Interests (RoIs). The proposed framework is evaluated on a public dataset DeepLesion and achieves results that surpass the active learning baseline schema at all the training cycles.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Budd, S., Robinson, E.C., Kainz, B.: A survey on active learning and human-in-the-loop deep learning for medical image analysis. arXiv preprint arXiv:1910.02923 (2019)
Budd, S., et al.: Confident head circumference measurement from ultrasound with real-time feedback for sonographers. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 683–691. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_75
Chen, X., Williams, B.M., Vallabhaneni, S.R., Czanner, G., Williams, R., Zheng, Y.: Learning active contour models for medical image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11632–11640 (2019)
Gal, Y., Islam, R., Ghahramani, Z.: Deep Bayesian active learning with image data. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1183–1192. JMLR.org (2017)
Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: a survey. arXiv preprint arXiv:1902.06162 (2019)
Károly, A.I., Fullér, R., Galambos, P.: Unsupervised clustering for deep learning: a tutorial survey. Acta Polytech. Hung. 15(8), 29–53 (2018)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Liu, J., Cao, L., Akin, O., Tian, Y.: 3DFPN-HS\(^2\): 3D feature pyramid network based high sensitivity and specificity pulmonary nodule detection. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 513–521. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_57
Lowell, D., Lipton, Z.C., Wallace, B.C.: Practical obstacles to deploying active learning. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 21–30 (2019)
Mahapatra, D., Bozorgtabar, B., Thiran, J.-P., Reyes, M.: Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 580–588. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_65
Nalisnik, M., Gutman, D.A., Kong, J., Cooper, L.A.: An interactive learning framework for scalable classification of pathology images. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 928–935. IEEE (2015)
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2020. CA: A Cancer J. Clin. 70(1), 7–30 (2020)
Wang, K., Zhang, D., Li, Y., Zhang, R., Lin, L.: Cost-effective active learning for deep image classification. IEEE Trans. Circ. Syst. Video Technol. 27(12), 2591–2600 (2016)
Wen, S., et al.: Comparison of different classifiers with active learning to support quality control in nucleus segmentation in pathology images. AMIA Summits on Transl. Sci. Proc. 2018, 227 (2018)
Wu, J., Qian, T.: A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques. J. Med. Artif. Intell. 2, 2–8 (2019)
Yan, K., Bagheri, M., Summers, R.M.: 3D context enhanced region-based convolutional neural network for end-to-end lesion detection. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 511–519. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_58
Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)
Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 399–407. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_46
Yoo, D., Kweon, I.S.: Learning loss for active learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 93–102 (2019)
Acknowledgement
This material is based upon work supported by the National Science Foundation under award number IIS-1400802.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, J., Cao, L., Tian, Y. (2020). Deep Active Learning for Effective Pulmonary Nodule Detection. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-59725-2_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59724-5
Online ISBN: 978-3-030-59725-2
eBook Packages: Computer ScienceComputer Science (R0)