# Clinical Priors Guided Lung Disease Detection in 3D CT Scans

arXiv: https://arxiv.org/abs/2603.15143 · HTML: https://mlsanity.com/paper/2603.15143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging](https://mlsanity.com/paper/2303.00175/) (2023)
- [Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans](https://mlsanity.com/paper/2403.02192/) (2024)
- [COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System](https://mlsanity.com/paper/2403.06242/) (2024)
- [SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection](https://mlsanity.com/paper/2407.15728/) (2024)
- [CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors](https://mlsanity.com/paper/2211.14557/) (2022)
- [Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification](https://mlsanity.com/paper/2211.14559/) (2022)
- [Advancing COVID-19 Detection in 3D CT Scans](https://mlsanity.com/paper/2403.11953/) (2024)
- [Multi-Source COVID-19 Detection via Variance Risk Extrapolation](https://mlsanity.com/paper/2506.23208/) (2025)
- [Advancing Lung Disease Diagnosis in 3D CT Scans](https://mlsanity.com/paper/2507.00993/) (2025)

## Abstract (arXiv)

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.