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datasets to train effectively. In practice, acquiring high-quality spectral data from wet-lab experiments is expensive and time-consuming. Furthermore, relying on a single spectral modality often
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datasets to train effectively. In practice, acquiring high-quality spectral data from wet-lab experiments is expensive and time-consuming. Furthermore, relying on a single spectral modality often
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- potentially offer great advantages over general purpose VLMs for a range of reasons. However there are fundamental questions over their performance and suitability for deployment inside healthcare, and
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- potentially offer great advantages over general purpose VLMs for a range of reasons. However there are fundamental questions over their performance and suitability for deployment inside healthcare, and
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Contribution The research aims to develop a robust, interpretable and generalisable AI framework for cardiovascular risk prediction and clinical decision support. The study will consider model performance
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Contribution The research aims to develop a robust, interpretable and generalisable AI framework for cardiovascular risk prediction and clinical decision support. The study will consider model performance
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Primary supervisor Chern Hong Lim Co-supervisors Bisan Alsalibi Yasmeen George Research area Data Science and Artificial Intelligence While deep learning has shown remarkable performance in medical
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publications including journal or conference of high repute Desirable Background in Machine Learning, Algorithms and Data Structures Project funding Other Learn more about minimum entry requirements .