Clinical Priors Guided Lung Disease Detection in 3D CT Scans
This work addresses class imbalance in medical imaging for computer-aided diagnosis systems, offering an incremental improvement by leveraging gender-specific classifiers.
The paper tackled the problem of class imbalance in lung disease classification from 3D CT scans by proposing a gender-aware two-stage framework that incorporates gender information to improve performance for minority categories, resulting in enhanced recognition for diseases like squamous cell carcinoma while maintaining competitive results on other classes.
Accurate classification of lung diseases from chest CT scans plays an important role in computer-aided diagnosis systems. However, medical imaging datasets often suffer from severe class imbalance, which may significantly degrade the performance of deep learning models, especially for minority disease categories. To address this issue, we propose a gender-aware two-stage lung disease classification framework. The proposed approach explicitly incorporates gender information into the disease recognition pipeline. In the first stage, a gender classifier is trained to predict the patient's gender from CT scans. In the second stage, the input CT image is routed to a corresponding gender-specific disease classifier to perform final disease prediction. This design enables the model to better capture gender-related imaging characteristics and alleviate the influence of imbalanced data distribution. Experimental results demonstrate that the proposed method improves the recognition performance for minority disease categories, particularly squamous cell carcinoma, while maintaining competitive performance on other classes.