Proceedings

Data-Centric AI for Chest X-Ray Analysis in Resource-Constrained Settings

Yasmeena Akhter

Download BibTeX Cite

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 10965-10966.

https://doi.org/10.24963/ijcai.2025/1237

Keywords

  • Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning
  • Multidisciplinary Topics and Applications: MDA: Computational sustainability
  • Computer Vision: CV: Machine learning for vision
  • Multidisciplinary Topics and Applications: MDA: Health and medicine
  • Machine Learning: ML: Applications
  • Machine Learning: ML: Attention models
  • Computer Vision: CV: Biomedical image analysis
  • Machine Learning: ML: Cost-sensitive learning
  • Machine Learning: ML: Classification
  • Machine Learning: ML: Ensemble methods
  • Computer Vision: CV: Applications
  • Machine Learning: ML: Multi-label
  • Machine Learning: ML: Convolutional networks
  • Computer Vision: CV: Representation learning
  • Machine Learning: ML: Representation learning
  • Machine Learning: ML: Multi-task and transfer learning

Abstract

With approximately 2 billion chest X-ray examinations conducted globally each year, the demand for radiological interpretation far surpasses the available expertise, particularly in resource-constrained regions. Recent advancements in artificial intelligence and computer vision present promising solutions for automated chest X-ray analysis. Nevertheless, integrating AI-driven diagnostics into clinical practice encounters several challenges, including data-centric issues, implementation barriers, deployment complexities, and the need for trustworthy AI. This dissertation focuses on the data-centric aspect, making significant contributions through enhanced data collection, the creation of novel datasets, algorithm development, privacy-preserving collaborative learning, and modelling for low-resolution data. It offers practical methodologies for embedding AI into chest radiology workflows, with a particular emphasis on addressing underserved conditions and healthcare settings with limited data availability. Furthermore, this work illustrates how tailored AI solutions can democratize access to high-quality radiological care while balancing privacy considerations and operational constraints across diverse environments.

Yasmeena Akhter. 2025. Data-Centric AI for Chest X-Ray Analysis in Resource-Constrained Settings. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10965–10966. https://doi.org/10.24963/ijcai.2025/1237
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards