Longitudinal Change Detection on Chest X-rays Using Geometric Correlation Maps

  • Dong Yul Oh
  • Jihang Kim
  • Kyong Joon LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


The diagnostic decision for chest X-ray image generally considers a probable change in a lesion, compared to the previous examination. We propose a novel algorithm to detect the change in longitudinal chest X-ray images. We extract feature maps from a pair of input images through two streams of convolutional neural networks. Next we generate the geometric correlation map computing matching scores for every possible match of local descriptors in two feature maps. This correlation map is fed into a binary classifier to detect specific patterns of the map representing the change in the lesion. Since no public dataset offers proper information to train the proposed network, we also build our own dataset by analyzing reports in examinations at a tertiary hospital. Experimental results show our approach outperforms previous methods in quantitative comparison. We also provide various case examples visualizing the effect of the proposed geometric correlation map.


Chest X-ray Longitudinal analysis Change detection Geometric correlation Neural network 



This work was supported by the Industrial Strategic technology development program (10072064) funded by the Ministry of Trade, Industry and Energy (MI, Korea) and by grant (no. 13-2019-006) from the SNUBH Research Fund.

Supplementary material

490281_1_En_83_MOESM1_ESM.pdf (1.7 mb)
Supplementary material 1 (pdf 1785 KB)


  1. 1.
    Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  2. 2.
    Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. arXiv preprint arXiv:1901.07031 (2019)
  3. 3.
    Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G.: Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4(6), 475 (2014)Google Scholar
  4. 4.
    Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 427–431. Association for Computational Linguistics, April 2017Google Scholar
  5. 5.
    Lakhani, P., Sundaram, B.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2), 574–582 (2017)CrossRefGoogle Scholar
  6. 6.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  7. 7.
    Nam, J.G., et al.: Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology 180237 (2018)Google Scholar
  8. 8.
    Rajpurkar, P., et al.: CheXnet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
  9. 9.
    Rocco, I., Arandjelovic, R., Sivic, J.: Convolutional neural network architecture for geometric matching. In: Proceedings of the CVPR, vol. 2 (2017)Google Scholar
  10. 10.
    Rocco, I., Arandjelovic, R., Sivic, J.: End-to-end weakly-supervised semantic alignment. In: Proceedings of the CVPR (2018)Google Scholar
  11. 11.
    Santeramo, R., Withey, S., Montana, G.: Longitudinal detection of radiological abnormalities with time-modulated LSTM. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 326–333. Springer, Cham (2018). Scholar
  12. 12.
    Setio, A.A.A., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5), 1160–1169 (2016)CrossRefGoogle Scholar
  13. 13.
    Singh, R., et al.: Deep learning in chest radiography: detection of findings and presence of change. PloS One 13(10), e0204155 (2018)CrossRefGoogle Scholar
  14. 14.
    Wang, F., et al.: Residual attention network for image classification. arXiv preprint arXiv:1704.06904 (2017)
  15. 15.
    Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 4 (2018)Google Scholar
  16. 16.
    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)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Interdisciplinary Program in BioengineeringSeoul National UniversitySeoulKorea
  2. 2.Department of RadiologySeoul National University Bundang HospitalSeongnam-siKorea

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