Addressing cross-population domain shift in chest X-ray classification through supervised adversarial domain adaptation

Addressing cross-population domain shift in chest X-ray classification through supervised adversarial domain adaptation

  • Bajwa, J., Munir, U., Nori, A. & Williams, B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthc J 8, e188–e194. (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Singh, V. K. et al. A computer-aided diagnosis system for breast cancer molecular subtype prediction in mammographic images. Elsevier eBooks 153–178, (2021).

  • Reardon, S. Rise of robot radiologists. Nature 576, S54–S58. (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    MATH 

    Google Scholar 

  • Ricci, A., Echeveste, R. & Ferrante, E. Addressing fairness in artificial intelligence for medical imaging. Nature Commun. (2022).

    Article 
    MATH 

    Google Scholar 

  • Vidal, C. Chest X-ray, (2019).

  • Shen, D., Wu, G. & Suk, H.-I. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248. (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 

  • Luo, Y., Zheng, L., Guan, T., Yu, J. & Yang, Y. Category-level adversaries for semantics consistent domain adaptation. arXiv.org (Taking a closer look at domain shift, 2018).

  • Deng, J. et al. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255 (IEEE, 2009).

  • Çallı, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K. G. & Murphy, K. Deep learning for chest X-ray analysis: A survey. Med. Image Anal. 72, 102125. (2021).

    Article 
    PubMed 

    Google Scholar 

  • Wang, X. et al. 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 (CVPR) (2017).

  • Johnson, A. E. W. et al. Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports. Sci. Data 6, 317. (2019).

    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 

  • Hong, J., Yu, S.C.-H. & Chen, W. Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning. Appl. Soft Comput. 121, 108729. (2022).

    Article 

    Google Scholar 

  • Xing, F., Bennett, T. D. & Ghosh, D. Adversarial domain adaptation and pseudo-labeling for cross-modality microscopy image quantification. Lect. Notes Comput. Sci. 11840, 740–749. (2019).

    Article 
    MATH 

    Google Scholar 

  • Musa, A., Ibrahim Adamu, M., Kakudi, H. A., Hernandez, M. & Lawal, Y. Analyzing cross-population domain shift in chest X-ray image classification and mitigating the gap with deep supervised domain adaptation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 585–595 (Springer, 2024).

  • Weninger, L., Liu, Q. & Merhof, D. Multi-task learning for brain tumor segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5, 327–337 (Springer, 2020).

  • Lenga, M., Schulz, H. & Saalbach, A. Continual learning for domain adaptation in chest X-ray classification (2020).

  • Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122-1131.e9. (2018).

    Article 
    CAS 
    PubMed 
    MATH 

    Google Scholar 

  • Wani, N. A., Kumar, R. & Bedi, J. Deepxplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence. Comput. Methods Programs Biomed. 243, 107879 (2024).

    Article 
    PubMed 
    MATH 

    Google Scholar 

  • Wani, N. A., Kumar, R., Bedi, J., Rida, I. et al. Explainable ai-driven iomt fusion: Unravelling techniques, opportunities, and challenges with explainable ai in healthcare. Information Fusion 102472 (2024).

  • Rasool, N., Bhat, J. I., Wani, N. A., Ahmad, N. & Alshara, M. Transresunet: Revolutionizing glioma brain tumor segmentation through transformer-enhanced residual unet. IEEE Access (2024).

  • Miglani, A. Fga-net: Feature-gated attention for glioma brain tumor segmentation in volumetric MRI images. Art. Intell. Knowl. Process. 66 (2024).

  • Musa, A., Adam, F. M., Ibrahim, U. & Zandam, A. Y. Learning from small datasets: An efficient deep learning model for covid-19 detection from chest X-ray using dataset distillation technique. In 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), 1–6, (2022).

  • Won Jo, S. & Seok, J. A study on deep learning-based classification for pneumonia detection. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), 1496–1498, (2022).

  • Vinoth, R., Subalakshmi, S. & Thamaraichandra, S. Pneumonia detection from chest X-ray using alexnet image classification technique. In 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), 1307–1312, (2023).

  • Tang, Y., Tang, Y., Sandfort, V., Xiao, J. & Summers, R. M. Tuna-net: Task-oriented unsupervised adversarial network for disease recognition in cross-domain chest X-rays. arXiv (Cornell University) (2019).

  • He, B., Chen, Y., Zhu, D. & Xu, Z. Domain adaptation via Wasserstein distance and discrepancy metric for chest X-ray image classification. Sci. Rep. (2024).

    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 

  • Ranjan, V., Harit, G. & Jawahar, C. Domain adaptation by aligning locality preserving subspaces. (2015).

  • arXiv.org. On the limits of cross-domain generalization in automated X-ray prediction. (MIDL, 2020).

  • Rajpurkar, P. et al. Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv (Cornell University) (2017).

  • Shelke, A. et al. Chest X-ray classification using deep learning for automated covid-19 screening. Sn Comput. Sci. 2, 300. (2021).

    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 

  • Chen, C. Improving the Domain Generalization and Robustness of Neural Networks for Medical Imaging. Ph.D. thesis, Imperial College London (2021).

  • Drukker, K. et al. Toward fairness in artificial intelligence for medical image analysis: Identification and mitigation of potential biases in the roadmap from data collection to model deployment. J. Med. Imaging 10, 061104–061104 (2023).

    Article 
    MATH 

    Google Scholar 

  • Hassan, E., Saber, A. & Elbedwehy, S. Knowledge distillation model for acute lymphoblastic leukemia detection: Exploring the impact of nesterov-accelerated adaptive moment estimation optimizer. Biomed. Signal Process. Control 94, 106246. (2024).

    Article 
    MATH 

    Google Scholar 

  • Saber, A., Elbedwehy, S., Awad, W. A. & Hassan, E. An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors. Neural Comput. Appl. 1–14 (2024).

  • Zhao, T. Seismic facies classification using different deep convolutional neural networks. Seg Tech. Program Expand. Abstr. (2018).

    Article 
    MATH 

    Google Scholar 

  • Ganin, Y. et al. Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17, 1–35 (2016).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Ganin, Y. & Lempitsky, V. Unsupervised domain adaptation by backpropagation. In International Conference on Machine Learning, 1180–1189 (PMLR, 2015).

  • He, G., Liu, X., Fan, F. & You, J. Classification-aware semi-supervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 964–965 (2020).

  • Liu, X. et al. Data augmentation via latent space interpolation for image classification. In 2018 24th International Conference on Pattern Recognition (ICPR), 728–733 (IEEE, 2018).

  • Wachinger, C. et al. Domain adaptation for Alzheimer’s disease diagnostics. Neuroimage 139, 470–479 (2016).

    Article 
    PubMed 

    Google Scholar 

  • Feng, Y. et al. Deep supervised domain adaptation for pneumonia diagnosis from chest S-ray images. IEEE J. Biomed. Health Inform. 26, 1080–1090. (2022).

    Article 
    PubMed 
    MATH 

    Google Scholar 

  • Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y. & Ghassemi, M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176–2182. (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Guan, H. & Liu, M. Domain adaptation for medical image analysis: A survey. IEEE Trans. Biomed. Eng. 69, 1173–1185 (2021).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Yu, M., Guan, H., Fang, Y., Yue, L. & Liu, M. Domain-prior-induced structural mri adaptation for clinical progression prediction of subjective cognitive decline. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 24–33 (Springer, 2022).

  • Pooch, E. H. P., Ballester, P. & Barros, R. C. Can we trust deep learning based diagnosis? the impact of domain shift in chest radiograph classification. In Petersen, J. et al. (eds.) Thoracic Image Analysis, 74–83 (Springer International Publishing, Cham, 2020).

  • Long, M., Cao, Y., Wang, J., Jordan, M. & Edu, J. Learning transferable features with deep adaptation networks (2015).

  • Thiam, P. et al. Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images. Front. Artific. Intell. 6, 1056422 (2023).

    Article 
    MATH 

    Google Scholar 

  • Kamnitsas, K. et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings 25, 597–609 (Springer, 2017).

  • He, B., Chen, Y., Zhu, D. & Xu, Z. Domain adaptation via wasserstein distance and discrepancy metric for chest X-ray image classification. Research Square (Research Square) (2023).

  • He, B., Chen, Y., Zhu, D. & Xu, Z. Domain adaptation via Wasserstein distance and discrepancy metric for chest X-ray image classification. Sci. Rep. 14, 2690 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 

  • Ghafoorian, M. et al. Transfer learning for domain adaptation in MRI: Application in brain lesion segmentation. In Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20, 516–524 (Springer, 2017).

  • Pan, S. J., Tsang, I. W., Kwok, J. T. & Yang, Q. Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22, 199–210 (2010).

    Article 
    PubMed 
    MATH 

    Google Scholar 

  • Sun, B., Feng, J. & Saenko, K. Return of frustratingly easy domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016).

  • Wang, J., Chen, Y., Hao, S., Feng, W. & Shen, Z. Balanced distribution adaptation for transfer learning. In 2017 IEEE International Conference on Data Mining (ICDM), 1129–1134 (IEEE, 2017).

  • Madani, A., Moradi, M., Karargyris, A. & Syeda-Mahmood, T. Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 1038–1042 (IEEE, 2018).

  • Tzeng, E., Hoffman, J., Saenko, K. & Darrell, T. Adversarial discriminative domain adaptation.

  • Stacke, K., Eilertsen, G., Unger, J. & Lundström, C. Measuring domain shift for deep learning in histopathology. IEEE J. Biomed. Health Inform. 25, 325–336. (2021).

    Article 
    PubMed 
    MATH 

    Google Scholar 

  • Sun, S., Shi, H. & Wu, Y. A survey of multi-source domain adaptation. Inf. Fusion 24, 84–92 (2015).

    Article 
    MATH 

    Google Scholar 

  • Vindr-cxr: An open dataset and benchmarks for disease classification and abnormality localization on chest radiographs | vindr (2020).

  • Rahman, T. Covid-19 radiography database (2020).

  • Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2261–2269, (2017).

  • Thiam, P. et al. Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images. Front. Artific. Intell. (2023).

    Article 
    MATH 

    Google Scholar 

  • Imran, A.-A.-Z. & Terzopoulos, D. Semi-supervised multi-task learning with chest X-ray images. In Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10, 151–159 (Springer, 2019).

  • Wang, W. et al. Rethinking maximum mean discrepancy for visual domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. 34, 264–277. (2023).

    Article 
    PubMed 
    MATH 

    Google Scholar 

  • Ouyang, L. & Key, A. Maximum mean discrepancy for generalization in the presence of distribution and missingness shift. arXiv preprint arXiv:2111.10344 (2021).

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