Clinical Priors Guided Lung Disease Detection in 3D CT Scans

TL;DR

This paper introduces a gender-aware two-stage framework for lung disease classification in 3D CT scans, improving minority class detection by incorporating gender information to address data imbalance.

Contribution

It presents a novel gender-aware approach that explicitly uses gender information to enhance disease classification accuracy in imbalanced datasets.

Findings

Improved detection of minority diseases like squamous cell carcinoma.

Enhanced overall classification performance with gender-specific models.

Effective handling of class imbalance in lung disease datasets.

Abstract

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.