Clinical Priors Guided Lung Disease Detection in 3D CT Scans

2026-03-16

AI Summary

This study addresses the performance degradation in identifying thoracic diseases from chest CT scans caused by severe class imbalance, particularly for underrepresented conditions such as squamous cell carcinoma. To mitigate this issue, the authors propose a gender-aware two-stage classification framework that first predicts patient sex and then routes the 3D CT image to a dedicated disease classifier tailored to that gender. By explicitly incorporating clinical prior knowledge—specifically, sex-based differences—the approach enables gender-specific modeling of pathological features. Experimental results demonstrate that the proposed framework significantly improves recognition accuracy for minority-class diseases while maintaining strong performance across other diagnostic categories, thereby effectively alleviating the adverse impact of data imbalance.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Medical and Biological Imaging
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• class imbalance
• lung disease detection
• minority disease categories
• 3D CT scans
• computer-aided diagnosis

Innovation

• gender-aware classification
• two-stage framework
• clinical priors
• class imbalance
• 3D CT scan