Hierarchical Multiple Instance Learning for COPD Grading with Relatively Specific Similarity

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 .