Abstract
Medical image classification datasets usually have a limited availability of annotated data, and pathological samples are usually much scarcer than healthy cases. Furthermore, data is often collected from different sources with different acquisition devices and population characteristics, making the trained models highly dependent on the data domain and thus preventing generalization. In this work, we propose to address these issues by combining transfer learning, data augmentation, a weighted loss function to balance the data, and domain adaptation. We evaluate the proposed approach on different chest X-Ray datasets labeled with COVID positive and negative diagnoses, yielding an average improvement of 15.3% in \({\text {F}}_1\) compared to the base case of training the model without considering these techniques. A 19.1% improvement is obtained in the intra-domain evaluation and a 7.7% for the inter-domain case.
This work was supported by the I+D+i project TED2021-132103A-I00 (DOREMI), funded by MCIN/AEI/10.13039/501100011033.
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Notes
- 1.
All the datasets considered are publicly available: ChestX-ray is available at https://nihcc.app.box.com/v/ChestXray-NIHCC, GitHub-COVID at https://github.com/ieee8023/covid-chestxray-dataset, PadChest can be found at https://bimcv.cipf.es/bimcv-projects/padchest, and BIMCV-COVID repositories are available at https://bimcv.cipf.es/bimcv-projects/bimcv-covid19.
References
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Gallego, A.J., Calvo-Zaragoza, J., Fisher, R.B.: Incremental unsupervised domain-adversarial training of neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 4864–4878 (2021). https://doi.org/10.1109/TNNLS.2020.3025954
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096–2030 (2016)
Garay-Maestre, U., Gallego, A.-J., Calvo-Zaragoza, J.: Data augmentation via variational auto-encoders. In: Vera-Rodriguez, R., Fierrez, J., Morales, A. (eds.) CIARP 2018. LNCS, vol. 11401, pp. 29–37. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13469-3_4
Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516–524. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_59
Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017). https://doi.org/10.1016/j.media.2016.05.004
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009). https://doi.org/10.1109/TKDE.2008.239
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Mitchell, T.M.: Machine Learning, vol. 1. McGraw-Hill, New York (1997)
Razzak, M.I., Naz, S., Zaib, A.: Deep learning for medical image processing: overview, challenges and the future. In: Dey, N., Ashour, A., Borra, S. (eds.) Classification in BioApps. LNCVB, vol. 26, pp. 323–350. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-65981-7_12
Rosello, A., Valero-Mas, J.J., Gallego, A.J., Sáez-Pérez, J., Calvo-Zaragoza, J.: Kurcuma: a kitchen utensil recognition collection for unsupervised domain adaptation. Pattern Anal. Appl. (2023). https://doi.org/10.1007/s10044-023-01147-x
Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016). https://doi.org/10.1109/TMI.2016.2528162
Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018). https://doi.org/10.1038/sdata.2018.161
Valero-Mas, J.J., Gallego, A.J., Alonso-Jiménez, P., Serra, X.: Multilabel prototype generation for data reduction in k-nearest neighbour classification. Pattern Recogn. 135, 109190 (2023). https://doi.org/10.1016/j.patcog.2022.109190
Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018). https://doi.org/10.1016/j.neucom.2018.05.083
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: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462–3471 (2017). https://doi.org/10.1109/CVPR.2017.369
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Galán-Cuenca, A., Mirón, M., Gallego, A.J., Saval-Calvo, M., Pertusa, A. (2023). Inter vs. Intra Domain Study of COVID Chest X-Ray Classification with Imbalanced Datasets. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_40
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