Abstract
During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. This variation is seen in the CXR projections used, image annotations added and in the inspiratory effort and degree of rotation of clinical images. The image analysis community has attempted to ease the burden on overstretched radiology departments during the pandemic by developing automated COVID-19 diagnostic algorithms, the input for which has been CXR imaging. Large publicly available CXR datasets have been leveraged to improve deep learning algorithms for COVID-19 diagnosis. Yet the variable quality of clinically-acquired CXRs within publicly available datasets could have a profound effect on algorithm performance. COVID-19 diagnosis may be inferred by an algorithm from non-anatomical features on an image such as image labels. These imaging shortcuts may be dataset-specific and limit the generalisability of AI systems. Understanding and correcting key potential biases in CXR images is therefore an essential first step prior to CXR image analysis. In this study, we propose a simple and effective step-wise approach to pre-processing a COVID-19 chest X-ray dataset to remove undesired biases. We perform ablation studies to show the impact of each individual step. The results suggest that using our proposed pipeline could increase accuracy of the baseline COVID-19 detection algorithm by up to 13%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Apostolopoulos, I.D., Mpesiana, T.A.: Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and engineering sciences in medicine 43(2), 635–640 (2020)
Cushnan, D., Bennett, O., Berka, R., Bertolli, O., Chopra, A., Dorgham, S., Favaro, A., Ganepola, T., Halling-Brown, M., Imreh, G., et al.: An overview of the national covid-19 chest imaging database: data quality and cohort analysis. GigaScience 10(11), giab076 (2021)
Cushnan, D., Berka, R., Bertolli, O., Williams, P., Schofield, D., Joshi, I., Favaro, A., Halling-Brown, M., Imreh, G., Jefferson, E., et al.: Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic. Digital Health 7, 20552076211048654 (2021)
DeGrave, A.J., Janizek, J.D., Lee, S.I.: Ai for radiographic covid-19 detection selects shortcuts over signal. Nature Machine Intelligence 3(7), 610–619 (2021)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. IEEE (2009)
Gunraj, H., Sabri, A., Koff, D., Wong, A.: Covid-net ct-2: Enhanced deep neural networks for detection of covid-19 from chest ct images through bigger, more diverse learning. arXiv preprint arXiv:2101.07433 (2021)
Heidari, M., Mirniaharikandehei, S., Khuzani, A.Z., Danala, G., Qiu, Y., Zheng, B.: Improving the performance of cnn to predict the likelihood of covid-19 using chest x-ray images with preprocessing algorithms. International journal of medical informatics 144, 104284 (2020)
Jacob, J., Alexander, D., Baillie, J.K., Berka, R., Bertolli, O., Blackwood, J., Buchan, I., Bloomfield, C., Cushnan, D., Docherty, A., et al.: Using imaging to combat a pandemic: rationale for developing the UK national covid-19 chest imaging database. European Respiratory Journal 56(2) (2020)
Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC medicine 17(1), 1–9 (2019)
Khan, A.I., Shah, J.L., Bhat, M.M.: Coronet: A deep neural network for detection and diagnosis of covid-19 from chest x-ray images. Computer Methods and Programs in Biomedicine 196, 105581 (2020)
Organization., W.H.: Coronavirus disease (covid-19): Variants of sars-cov-2 (2021)
Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of covid-19 cases using deep neural networks with x-ray images. Computers in biology and medicine 121, 103792 (2020)
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., dAlché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019), http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
Rahimzadeh, M., Attar, A.: A modified deep convolutional neural network for detecting covid-19 and pneumonia from chest x-ray images based on the concatenation of xception and resnet50v2. Informatics in medicine unlocked 19, 100360 (2020)
Selvan, R., Dam, E.B., Detlefsen, N.S., Rischel, S., Sheng, K., Nielsen, M., Pai, A.: Lung segmentation from chest x-rays using variational data imputation. arXiv preprint arXiv:2005.10052 (2020)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp. 618–626 (2017)
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105–6114. PMLR (2019)
Toraman, S., Alakus, T.B., Turkoglu, I.: Convolutional capsnet: A novel artificial neural network approach to detect covid-19 disease from x-ray images using capsule networks. Chaos, Solitons & Fractals 140, 110122 (2020)
Acknowledgements
This research was supported by Wellcome Trust Clinical Research Career Development Fellowship 209553/Z/17/Z and the NIHR UCLH Biomedical Research Centre, UK. For the purpose of open access, the author has applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission.
Author information
Authors and Affiliations
Consortia
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Aslani, S. et al. (2023). Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors. In: Su, R., Zhang, Y., Liu, H., F Frangi, A. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2022. Lecture Notes in Electrical Engineering, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-16-6775-6_20
Download citation
DOI: https://doi.org/10.1007/978-981-16-6775-6_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6774-9
Online ISBN: 978-981-16-6775-6
eBook Packages: MedicineMedicine (R0)