bibtext: @InProceedings{ Mus_Analyzing_MICCAI2024,
                   author = { Musa, Aminu and Ibrahim Adamu, Mariya and Kakudi, Habeebah Adamu and Hernandez, Monica and Lawal, Yusuf },
                   title = { { Analyzing Cross-Population Domain Shift in Chest X-Ray Image Classification and Mitigating the Gap with Deep Supervised Domain Adaptation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: Medical image analysis powered by artificial intelligence (AI) is pivotal in healthcare diagnostics. However, the efficacy of machine learning models relies on their adaptability to diverse patient populations, presenting domain shift challenges. This study investigates domain shift in chest X-ray classification, focusing on cross-population variations, specifically in African dataset. Disparities between source and target populations were measured by evaluating model performance. We propose supervised domain adaptation to mitigate this issue, leveraging labeled data in both domains for fine-tuning. Our experiments show significant improvements in model accuracy for chest X-ray classification in the African dataset. This research underscores the importance of domain-aware model development in AI-driven healthcare, contributing to addressing domain-shift challenges in medical imaging.
title: Analyzing Cross-Population Domain Shift in Chest X-Ray Image Classification and Mitigating the Gap with Deep Supervised Domain Adaptation
authors: Musa, Aminu Ibrahim Adamu, Mariya Kakudi, Habeebah Adamu Hernandez, Monica Lawal, Yusuf