Machine Learning and Domain Knowledge in Biomedical Imaging

Dr. Omondi James Okeda and Roseline Aduda
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Dr. Omondi James Okeda: Department of Information Technology, Uzima University
Roseline Aduda: Department of Information Technology, Uzima University

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 5, 5618-5647

Abstract: This analysis explores the integration of machine learning techniques and domain knowledge within the realm of biomedical imaging, emphasizing three pivotal areas: feature extraction, computational modeling, and annotation-efficient learning. Biomedical images contain complex and high-dimensional data, wherein effective feature extraction methods that incorporate expert insights can substantially improve downstream analysis. Computational models leveraging both data-driven algorithms and domain-specific information enable more accurate interpretation and predictive capabilities in various biomedical applications. The primary objective of this study is to investigate how combining machine learning with domain expertise enhances the efficacy and robustness of biomedical image analysis. Specifically, it addresses challenges related to limited labeled datasets by focusing on annotation-efficient learning approaches that reduce dependency on extensive manual annotation while maintaining performance. The methodology includes an extensive literature review, quantitative evaluations, and case study analyses, highlighting recent advances and practical implementations. Key findings demonstrate that the fusion of domain knowledge and machine learning significantly improves feature representation quality, model interpretability, and generalization across diverse biomedical imaging tasks. Annotation-efficient strategies, such as semi-supervised and weakly supervised learning, effectively leverage sparse labels without sacrificing accuracy. Moreover, computational modeling that synergizes mechanistic understanding with statistical learning contributes to more reliable diagnostic and prognostic tools. Overall, this comprehensive examination underscores the critical role of integrating expert knowledge with advanced machine learning frameworks in biomedical imaging. The insights gained can guide future research and development efforts aimed at enhancing image-based healthcare solutions through efficient and scalable analytical pipelines.

Date: 2025
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