{"ID":2858711,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06887","arxiv_id":"2510.06887","title":"Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention","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.","short_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 bot...","url_abs":"https://arxiv.org/abs/2510.06887","url_pdf":"https://arxiv.org/pdf/2510.06887v1","authors":"[\"Bouthaina Slika\",\"Fadi Dornaika\",\"Fares Bougourzi\",\"Karim Hammoudi\"]","published":"2025-10-08T11:08:34Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":608574,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2858711,"paper_url":"https://arxiv.org/abs/2510.06887","paper_title":"Lung Infection Severity Prediction Using Transformers with Conditional TransMix Augmentation and Cross-Attention","repo_url":"https://github.com/bouthainas/QCross-Att-PVT","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}