Krams, RobAlharthi, Hatem2024-10-272024-08Harvardhttps://hdl.handle.net/20.500.14154/73329, , , , ,This research investigates the optimisation of a Domain-Adversarial Neural Network (DANN) for automated classification of five atherosclerotic plaque types using intracoronary Optical Coherence Tomography (OCT) pullbacks. Leveraging histologically co-registered labelled pig OCT images and unlabelled human OCT images obtained from Coronary Artery Disease (CAD) patients, the research focused on enhancing DANN’s ability to extract domain-invariant feature representations, thus adapt to domain shifts. A key innovative fine-tuning strategy was implemented using selective fine-tuning of the last four layers of a pre-trained DenseNet-121 model, which significantly improved the model's performance, achieving an average AUC-ROC of 0.935. The incorporation of a Gradient Reversal Layer (GRL) effectively mitigates domain discrepancies, as evidenced by a decrease in Proxy A-Distance from 2.0 to 0.66, and clearly visualised using t-SNE. The model demonstrated high testing sensitivity and specificity across all plaque types and specifically in identifying Thin-Cap Fibroatheroma (TCFA) plaque with 100% accuracy and sensitivity on our pig source data, indicating its potential for clinical application in cardiology. While the study acknowledges limitations such as dataset size and the empirical approach to model tuning, the findings contribute valuable insights into the role of domain adaptation in medical imaging, offering a robust framework for future research and clinical implementation.46enAI Decision-Making ToolDomain-Adversarial Neural Networks (DANN)Optical Coherence Tomography (OCT)Atherosclerosis ClassificationUnsupervised Domain Adaptation (UDA)Thin-Cap Fibroatheroma (TCFA)Deep Learning in Medical ImagingTransfer LearningCoronary Artery Disease (CAD)Gradient Reversal Layer (GRL)Thesis