## 2 The Papers
### Applications-Cardiovascular Diseases  
Cardiovascular ailment (CVD) poses a substantial peril to human well-being and stands as the primary global fatality determinant [10]. The incidence of both mortality and morbidity linked to CVD exhibits an escalating trajectory, particularly within burgeoning territories. This malady precipitates considerable financial ramifications, approximated at 351.2 billion USD in the United States, thereby engendering persistent compromise to the quality of life [11]. Within the European Union, the annual expenditure has been assessed at 210 billion euros, apportioned amongst direct healthcare outlays (53%), diminished productivity (26%), and informal caregiving for individuals afflicted with CVD (21%) [12]. In the field of cardiovascular disease applications four papers are presented. The first paper in this field proposes two methods for binary classifying the risk of CAD based on the CAC (CAC \(>\) 400 represents high risk of CAD, while CAC \(<\) 400 low risk of CAD) in diabetic patients [13]: the first method consists in employing a state-of-the-art CNN architecture for CAD risk assessment, based on the retina images and the second method consists in employing classical machine learning classifiers on the clinical data (age and presence of diabetic retinopathy). The DL algorithm considered therein is a VGG16 architecture trained on ImageNet and finetuned on the available retina images. By using the proposed methods, two protocols were established that target two specific applications. The statistics (accuracy, precision, recall, F1 score, confusion matrix) were computed to evaluate each method and the protocols. Results show acceptable accuracies when evaluating the methods independently, while when combining the methods either the precision or recall improve depending on the protocol (the protocol that is created based on the particular needs of each application). The second paper focuses on obtaining a smaller processing time when using a semi-automated approach for the task of segmenting coronary artery lumen by pre-selecting vessel locations likely to require manual inspection and editing [14]. The pre-selection step is formulated as an Out-of-Distribution (OoD) detection problem with the task of detecting mismatched pairs of CCTA lumen images and their corresponding lumen segmentations. Two Normalizing Flows architectures are employed and assessed: a Glow-like baseline, and a NF architecture which uses a novel coupling layer which exhibits an inductive bias favoring the exploitation of semantical features instead of local pixel correlations. The models were assessed on both synthetic mask perturbations and expert annotations. On synthetic perturbations, the results indicate a better performance for the proposed model, when compared with the baseline model. The proposed model also outperforms the baseline, having a sensitivity for detecting faulty annotations close to inter-expert agreement. The main objective in the third paper is to evaluate the feasibility of using neural networks in predicting invasively measured FFR from the radius of the coronary lumen that is extracted along the centerline of the coronary artery from OCT images [15]. Three different approaches were used for solving this task: a regression, a classification and an FSL (few shot learning) approach, where the task was formulated also as a classification problem. For each approach different types of architectures were considered: ANN, CNN and RNN. The evaluation step is performed on ensembles for each architecture type: each proposed architecture is trained 20 times, with different random seeds, and the final prediction is performed by the mean value (for regression)/probabilities (for classification) of all 20 models. The FSL CNN based ensemble shows the best diagnostic performance, while being the most robust approach by having the smallest standard deviation and uncertainty. Moreover, compared with baseline approaches based on MLD or %DS FSL CNN reaches improved results.