Anna Feleki
University of Thessaly
11 Papers
2 Citations
Anna Feleki is an academic researcher from University of Thessaly. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 1, co-authored 2 publications.
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Papers
A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
TL;DR: A robust CNN model is built that will be able to classify images of whole-body scans in patients suffering from breast cancer, depending on whether or not they are infected by metastasis of breast cancer.
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Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images
TL;DR: This research addresses the supervised learning-based ideal observer image classification utilizing an RGB-CNN model in heart images to diagnose CAD and proves that the newly developed deep learning models may be of great assistance in nuclear medicine and clinical decision-making.
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Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies
Ioannis D. Apostolopoulos,Nikolaos Papandrianos,Anna Feleki,Serafeim Moustakidis,Elpiniki I. Papageorgiou +4 more
TL;DR: In this article , a review of recent DL approaches focused on cardiac SPECT imaging is presented, which distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction.
Deep learning exploration for SPECT MPI polar map images classification in coronary artery disease
Nikolaos Papandrianos,Ioannis D. Apostolopoulos,Anna Feleki,Dimitris J. Apostolopoulos,Elpiniki I. Papageorgiou +4 more
TL;DR: The exploration and the implementation of a deep learning method using a state-of-the-art convolutional neural network for the classification of polar maps represent myocardial perfusion for the detection of coronary artery disease and the proposed model could be an effective tool for medical classification problems, in the case of polar map data acquired from myocardIAL perfusion images.
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Explainable Deep Fuzzy Cognitive Map Diagnosis of Coronary Artery Disease: Integrating Myocardial Perfusion Imaging, Clinical Data, and Natural Language Insights
Anna Feleki,Ioannis D. Apostolopoulos,Serafeim Moustakidis,Elpiniki I. Papageorgiou,Nikolaos Papathanasiou,Dimitrios Apostolopoulos,Nikolaos Papandrianos +6 more
TL;DR: The Deep Fuzzy Cognitive Map (DeepFCM), a novel, transparent, and explainable model designed to diagnose CAD using imaging and clinical data, and employs the Generative Pre-trained Transformer version 3.5 model to generate meaningful explanations for medical staff.
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