Abstract
Automatic and accurate analysis in biomedical images (e.g., image classification, lesion detection and segmentation) plays an important role in computer-aided diagnosis of common human diseases. However, this task is challenging due to the need of sufficient training data with high quality annotation, which is both time-consuming and costly to obtain. In this chapter, we propose a novel Deep Active Self-paced Learning (DASL) strategy to reduce annotation effort and also make use of unannotated samples, based on a combination of Active Learning (AL) and Self-Paced Learning (SPL) strategies. To evaluate the performance of the DASL strategy, we apply it to two typical problems in biomedical image analysis, pulmonary nodule segmentation in 3D CT images and diabetic retinopathy (DR) identification in digital retinal fundus images. In each scenario, we propose a novel deep learning model and train it with the DASL strategy. Experimental results show that the proposed models trained with our DASL strategy perform much better than those trained without DASL using the same amount of annotated samples.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Gonçalves, L., Novo, J., Campilho, A.: Hessian based approaches for 3D lung nodule segmentation. Expert. Syst. Appl. 61, 1–15 (2016)
Armato III, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)
Messay, T., Hardie, R.C., Tuinstra, T.R.: Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset. Med. Image Anal. 22(1), 48–62 (2015)
Feng, X., Yang, J., Laine, A.F., Angelini, E.D.: Discriminative localization in CNNs for weakly-supervised segmentation of pulmonary nodules. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 568–576. Springer (2017)
Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 399–407. Springer (2017)
Dai, L., Sheng, B., Wu, Q., Li, H., Hou, X., Jia, W., Fang, R.: Retinal microaneurysm detection using clinical report guided multi-sieving CNN. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 525–532. Springer (2017)
van Grinsven, M.J.J.P., van Ginneken, B., Hoyng, C.B., Theelen, T., Sanchez, C.I.: Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016)
Seoud, L., Hurtut, T., Chelbi, J., Cheriet, F., Langlois, J.M.P.: Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans. Med. Imaging 35(4), 1116–1126 (2015)
Yang, Y., Li, T., Li, W., Wu, H., Fan, W., Zhang, W.: Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 533–540. Springer (2017)
Li, X., Zhong, A., Lin, M., Guo, N., Sun, M., Sitek, A., Ye, J., Thrall, J., Li, Q.: Self-paced convolutional neural network for computer aided detection in medical imaging analysis. In: International Workshop on Machine Learning in Medical Imaging, pp. 212–219. Springer (2017)
Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin – Madison (2009)
Kumar, M.P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: Advances in Neural Information Processing Systems, pp. 1189–1197 (2010)
Lin, L., Wang, K., Meng, D., Zuo, W., Zhang, L.: Active self-paced learning for cost-effective and progressive face identification. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 7–19 (2018)
Wang, W., Lu, Y., Wu, B., Chen, T., Chen, D.Z., Wu, J.: Deep active self-paced learning for accurate pulmonary nodule segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 723–731. Springer (2018)
Lin, Z., Guo, R., Wang, Y., Wu, B., Chen, T., Wang, W., Chen, D.Z., Wu, J.: A framework for identifying diabetic retinopathy based on anti-noise detection and attention-based fusion. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 74–82. Springer (2018)
Jiang, L., Meng, D., Zhao, Q., Shan, S., Hauptmann, A.G.: Self-paced curriculum learning. In: AAAI Conference on Artificial Intelligence, pp. 2694–2700 (2015)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 424–432. Springer (2016)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: 4th IEEE International Conference on 3D Vision, pp. 565–571. IEEE (2016)
Yu, L., Cheng, J.Z., Dou, Q., Yang, X., Chen, H., Qin, J., Heng, P.A.: Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 287–295. Springer (2017)
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision, pp. 2980–2988. IEEE (2017)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Lin, T.Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 936–944. IEEE (2017)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708. IEEE (2017)
Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision, pp. 1440–1448. IEEE (2015)
Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Deep Learning and Data Labeling for Medical Applications, pp. 179–187. Springer (2016)
Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: European Conference on Computer Vision, pp. 499–515. Springer (2016)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: Single shot multibox detector. In: European conference on computer vision, pp. 21–37. Springer (2016)
Chen, L.C., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: Scale-aware semantic image segmentation. In: Computer Vision and Pattern Recognition, pp. 3640–3649. IEEE (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778. IEEE (2016)
Acknowledgements
The research of Jian Wu was partially supported by the Ministry of Education of China under grant No. 2017PT18, the Zhejiang University Education Foundation under grants No. K18-511120-004, No. K17-511120-017, and No. K17-518051-021, the Major Scientific Project of Zhejiang Lab under grant No. 2018DG0ZX01, and the National Natural Science Foundation of China under grant No. 61672453. The research of Danny Z. Chen was supported in part by NSF Grant CCF-1617735.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Wang, W. et al. (2020). Deep Active Self-paced Learning for Biomedical Image Analysis. In: Chen, YW., Jain, L. (eds) Deep Learning in Healthcare. Intelligent Systems Reference Library, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-030-32606-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-32606-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32605-0
Online ISBN: 978-3-030-32606-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)