Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention

• URL: http://arxiv.org/abs/2510.06887v1
• Date: Wed, 08 Oct 2025 11:08:34 GMT
• Title: Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention
• Authors: Bouthaina Slika, Fadi Dornaika, Fares Bougourzi, Karim Hammoudi,
• Abstract summary: Lung infections, particularly pneumonia, pose serious health risks that can escalate rapidly.<n>We present a novel method applicable to both CT scans and chest X-rays for assessing lung infection severity.<n>Our contributions are twofold: (i) QCross-Att-PVT, a Transformer-based architecture that integrates parallel encoders, a cross-gated attention mechanism, and a feature aggregator, and (ii) Conditional Online TransMix, a custom data augmentation strategy.
• Score: 13.087848666528563
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Lung infections, particularly pneumonia, pose serious health risks that can escalate rapidly, especially during pandemics. Accurate AI-based severity prediction from medical imaging is essential to support timely clinical decisions and optimize patient outcomes. In this work, we present a novel method applicable to both CT scans and chest X-rays for assessing lung infection severity. Our contributions are twofold: (i) QCross-Att-PVT, a Transformer-based architecture that integrates parallel encoders, a cross-gated attention mechanism, and a feature aggregator to capture rich multi-scale features; and (ii) Conditional Online TransMix, a custom data augmentation strategy designed to address dataset imbalance by generating mixed-label image patches during training. Evaluated on two benchmark datasets, RALO CXR and Per-COVID-19 CT, our method consistently outperforms several state-of-the-art deep learning models. The results emphasize the critical role of data augmentation and gated attention in improving both robustness and predictive accuracy. This approach offers a reliable, adaptable tool to support clinical diagnosis, disease monitoring, and personalized treatment planning. The source code of this work is available at https://github.com/bouthainas/QCross-Att-PVT.

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