Abstract
Chronic obstructive pulmonary disease (COPD) is a type of obstructive lung disease characterized by persistent airflow limitation and ranks as the third leading cause of death globally. As a heterogeneous lung disorder, the diversity of COPD phenotypes and the complexity of its pathology pose significant challenges for recognizing its grade. Many existing deep learning models based on 3D CT scans overlook the spatial position information of lesion regions and the correlation within different lesion grades. To this, we define the COPD grading task as a multiple instance learning (MIL) task and propose a hierarchical multiple instance learning (H-MIL) model. Unlike previous MIL models, our H-MIL model pays more attention to the spatial position information of patches and achieves a fine-grained classification of COPD by extracting patch features in a multi-level and granularity-oriented manner. Furthermore, we recognize the significant correlations within lesions of different grades and propose a Relatively Specific Similarity (RSS) function to capture such relative correlations. We demonstrate that H-MIL achieves better performances than comparative methods on an internal dataset comprising 2,142 CT scans. Additionally, we validate the effectiveness of the model architecture and loss design through an ablation study. and the robustness of our model on different central datasets. Code is available at https://github.com/Mars-Zhang123/H-MIL.git.
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
References
Ahmed, J., et al.: COPD classification in CT images using a 3D convolutional neural network. In: Bildverarbeitung für die Medizin 2020. I, pp. 39–45. Springer, Wiesbaden (2020). https://doi.org/10.1007/978-3-658-29267-6_8
Bhatt, S.P., et al.: Discriminative accuracy of FEV1: FVC thresholds for COPD-related hospitalization and mortality. JAMA 321(24), 2438–2447 (2019)
Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301–1309 (2019)
El Kaddouri, B., et al.: Fleischner society visual emphysema CT patterns help predict progression of emphysema in current and former smokers: results from the COPDGene study. Radiology 298(2), 441–449 (2021)
Fu, J., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146–3154 (2019)
Gershon, A.S., et al.: Health services burden of undiagnosed and over diagnosed COPD. Chest 153(6), 1336–1346 (2018)
Gonzalez, G., et al.: Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am. J. Respir. Crit. Care Med. 197(2), 193–203 (2018)
Guthrie, A.: Chronic obstructive pulmonary disease series Part 4: identifying, managing, and preventing exacerbations. Sr. Care Pharm. 38(9), 361–369 (2023)
Hatt, C., Galban, C., Labaki, W., Kazerooni, E., Lynch, D., Han, M.: Convolutional neural network based COPD and emphysema classifications are predictive of lung cancer diagnosis. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA -2018. LNCS, vol. 11040, pp. 302–309. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00946-5_30
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Humphries, S.M., et al.: Deep learning enables automatic classification of emphysema pattern at CT. Radiology 294(2), 434–444 (2020)
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127–2136. PMLR (2018)
Oelsner, E.C., et al.: Association between emphysema-like lung on cardiac computed tomography and mortality in persons without airflow obstruction: a cohort study. Ann. Intern. Med. 161(12), 863–873 (2014)
Park, J., et al.: Subtyping COPD by using visual and quantitative CT imaging features. Chest 157(1), 47–60 (2020)
Riley, C.M., Sciurba, F.C.: Diagnosis and outpatient management of chronic obstructive pulmonary disease: a review. JAMA 321(8), 786–797 (2019)
Schroeder, J.D., et al.: Prediction of obstructive lung disease from chest radiographs via deep learning trained on pulmonary function data. Int. J. Chron. Obstruct. Pulmon. Dis. 15, 3455–3466 (2020)
Singh, D., et al.: Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: the gold science committee report 2019. Eur. Respir. J. 53(5) (2019)
Soriano, J.B., Polverino, F., Cosio, B.G.: What is early COPD and why is it important? Eur. Respir. J. 52(6), 1801448 (2018)
Steiger, D., et al.: The importance of low-dose CT screening to identify emphysema in asymptomatic participants with and without a prior diagnosis of COPD. Clin. Imaging 78, 136–141 (2021)
Sun, J., et al.: Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach. Eur. Radiol. 32(8), 5319–5329 (2022)
Tang, L.Y., Coxson, H.O., Lam, S., Leipsic, J., Tam, R.C., Sin, D.D.: Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT. Lancet Digit. Health 2(5), e259–e267 (2020)
Venkatesan, P.: Gold COPD report: 2024 update. Lancet Respir. Med. 12(1), 15–16 (2024)
Zhang, H., et al.: DTFD-MIL: double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18802–18812 (2022)
Zhang, L., Jiang, B., Wisselink, H.J., Vliegenthart, R., Xie, X.: COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images. Br. J. Radiol. 95(1133), 20210637 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, H. et al. (2024). Hierarchical Multiple Instance Learning for COPD Grading with Relatively Specific Similarity. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15001. Springer, Cham. https://doi.org/10.1007/978-3-031-72378-0_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-72378-0_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72377-3
Online ISBN: 978-3-031-72378-0
eBook Packages: Computer ScienceComputer Science (R0)
