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Assisting Radiologists in X-Ray Diagnostics

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IoT Technologies for HealthCare (HealthyIoT 2019)

Abstract

Studies have shown that radiologists working together with Computer Aided Diagnostic software have increased accuracy. Automated screening software can be used to prioritize X-Rays coming in for diagnosis. We developed a suite of machine learning algorithms that aim to improve radiologist performance. It provides suggested diagnostics, a heatmap showing pathological areas and a bone subtracted version of the image which helps radiologists to identify fractures. We test different configurations for our diagnosis model, training it on both normal and enhanced images, using one or two branches. Our experiments show that adding enhanced inputs (lung segmented and bone subtracted versions of the input) increases the performance of our algorithm, which in turn increases the performance of the radiologist user. This shows that preprocessing the images before input increases model performance. More research is needed to find other preprocessing techniques, to refine existing ones, and to determine the optimal number and type of input X-Rays.

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References

  1. PAHO WHO. World Radiography Day: Two-Thirds of the World’s Population has no Access to Diagnostic Imaging. PAHO (2012)

    Google Scholar 

  2. Stec, N., Arje, D., Moody, A.R., Krupinski, E.A., Tyrrell, P.N.: A systematic review of fatigue in radiology: is it a problem? AJR Am. J. Roentgenol. 210(4), 799–806 (2018)

    Article  Google Scholar 

  3. Rajpurkar, P., et al.: CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv e-prints, art. arXiv:1711.05225, November 2017

  4. Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., Lyman, K.: Learning to diagnose from scratch by exploiting dependencies among labels. arXiv e-prints, art. arXiv:1710.10501, October 2017

  5. Kumar, P., Grewal, M., Srivastava, M.M.: Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs. arXiv e-prints, art. arXiv:1711.08760, November 2017

  6. Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., Yang, Y.: Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification. arXiv e-prints, art. arXiv:1801.09927, January 2018

  7. 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. arXiv e-prints, art. arXiv:1705.02315, May 2017

  8. Irvin, J., et al.: CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. arXiv e-prints, art. arXiv:1901.07031, January 2019

  9. Johnson, A.E.W., et al.: MIMIC-CXR: a large publicly available database of labeled chest radiographs. arXiv e-prints, art. arXiv:1901.07042, January 2019

  10. Armato III, S.G., Giger, M.L., MacMahon, H.: Automated lung segmentation in digitized posteroanterior chest radiographs. Acad. Radiol. 5(4), 245–255 (1998)

    Article  Google Scholar 

  11. Rashid, R., Akram, M.U., Hassan, T.: Fully convolutional neural network for lungs segmentation from chest X-rays. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 71–80. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_9. ISBN 978-3-319-93000-8

    Chapter  Google Scholar 

  12. Frid-Adar, M., Ben-Cohen, A., Amer, R., Greenspan, H.: Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder. arXiv e-prints, art. arXiv:1810.02113, October 2018

  13. Loog, M., van Ginneken, B., Schilham, A.M.R.: Filter learning: application to suppression of bony structures from chest radiographs. Med. Image Anal. 10(6), 826–840 (2006). https://doi.org/10.1016/j.media.2006.06.002. http://www.sciencedirect.com/science/article/pii/S1361841506000454. ISSN 1361-8415

    Article  Google Scholar 

  14. Chen, S., Suzuki, K.: Separation of bones from chest radiographs by means of anatomically specific multiple massive-training anns combined with total variation minimization smoothing. IEEE Trans. Med. Imaging 33(2), 246–257 (2014). https://doi.org/10.1109/TMI.2013.2284016

    Article  Google Scholar 

  15. Zhou, B., Lin, X., Eck, B., Hou, J., Wilson, D.: Generation of virtual dual energy images from standard single-shot radiographs using multi-scale and conditional adversarial network. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11361, pp. 298–313. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20887-5_19

    Chapter  Google Scholar 

  16. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks. arXiv e-prints, art. arXiv:1608.06993, August 2016

  17. He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of Tricks for Image Classification with Convolutional Neural Networks. arXiv e-prints, art. arXiv:1812.01187, December 2018

  18. Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv e-prints, art. arXiv:1505.04597, May 2015

  19. Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv e-prints, art. arXiv:1409.1556, September 2014

  20. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-Attention Generative Adversarial Networks. arXiv e-prints, art. arXiv:1805.08318, May 2018

  21. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning Deep Features for Discriminative Localization. arXiv e-prints, art. arXiv:1512.04150, December 2015

  22. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009 (2009)

    Google Scholar 

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Correspondence to Stefan Iarca .

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Avramescu, C., Bogdan, B., Iarca, S., Tenescu, A., Fuicu, S. (2020). Assisting Radiologists in X-Ray Diagnostics. In: Garcia, N., Pires, I., Goleva, R. (eds) IoT Technologies for HealthCare. HealthyIoT 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 314. Springer, Cham. https://doi.org/10.1007/978-3-030-42029-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-42029-1_8

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  • Online ISBN: 978-3-030-42029-1

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