Diagnosis of Coronary Artery Disease from Myocardial Perfusion Imaging Polar Maps with an innovative attention-based feature-fusion network
Accepted: MLDM2023
Conference Proceedings
Explainable prediction of coronary artery disease in nuclear medical imaging using deep learning
Accepted: EANM2022 Aim/Introduction: Coronary artery disease (CAD), also called ischemic heart disease, is the leading cause of death worldwide. Early CAD detection is unavoidably critical for the patient’s health and, more specifically, for the determination of an effective treatment. To enable reliable and trustworthy decisions, nuclear imaging specialists would require an autonomous diagnostic tool that
Deep learning for automatic diagnosis of coronary artery disease using SPECT MPI images
Accepted: EANM2022 Aim/Introduction: Coronary Artery Disease (CAD) exhibits the highest mortality rate among various heart diseases. The severity of CAD has enforced research to provide support from computer-aided systems of near-optimal accuracy in image classification and anomaly detection functionalities. Our research addresses the three-class image classification in CAD to detect normal state, ischemia or infarction,
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A Convolutional Neural Network model for SPECT Myocardial Perfusion Images Classification
Accepted: IWBBIO2022 This research work addresses the problem of coronary artery disease (CAD) diagnosis using Single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) data, by applying deep learning techniques. We propose a Convolutional Neural Network implementation to classify SPECT-MPI images fully automatically, also utilizing transfer learning employing VGG-16 as a pre-trained network, for
A Convolutional Neural Network-based explainable classification method of SPECT myocardial perfusion images in nuclear cardiology
Accepted: IISA2022 This study targets on the development of an explainable Convolutional Neural Network (CNN) pipeline in the form of a handcrafted CNN to identify patients’ coronary artery disease status (normal, ischemia or infarction). The proposed RGB-CNN model utilizes various pre- and post-processing tools and deploys a state-of-the-art explainability tool to produce more interpretable predictions
