Towards adversarial robustness with 01 lossmodels, and novel convolutional neural netsystems for ultrasound images

This dissertation investigates adversarial robustness with 01 loss models and a novel convolutional neural net systems for vascular ultrasound images. In the first part, the dissertation presents stochastic coordinate descent for 01 loss and its sensitivity to adversarial attacks. The study here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required. In the second part, this dissertation proposes sign activation network with a novel gradient-free stochastic coordinate descent algorithm and its ensembling model. The study here finds that the ensembling model gives a high minimum distortion (as measured by HopSkipJump) compared to full precision, binary, and convolutional neural networks, and explains this phenomenon by measuring the transferability between networks in an ensemble. In the last part, this dissertation tackles three important segmentation problems for vascular ultrasound images with novel convolutional neural networks. More specifically, these three problems are: (1) vessel segmentation in the internal carotid artery, (2) vessel segmentation in the entire carotid system, and (3) vessel and plaque segmentation in the entire carotid system. The study here represents a first successful step towards the automated segmentation of vessel and plaque in carotid artery ultrasound images and is an important step in creating a system that can independently evaluate carotid ultrasounds

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Deep learning for the detection and characterization of the carotid artery in ultrasound imaging

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Tutor: Laura Igual Muñoz[en] Atherosclerosis is the main process causing most Cardio Vascular (CV) diseases. The measurement of Intima Media Thickness (IMT) in artery ultrasound images can be used to detect the presence of atherosclerotic plaques, which may appear in several territories of the artery. Moreover, it is well known that disruption of atherosclerotic plaque plays a crucial role in the pathogenesis of CV events. Several works have tried to automatize the detection of the IMT and the classification of the plaque by its composition. Traditionally, the methods used in the literature are semi-automatic. Furthermore, very little work has been done using Deep Learning approaches in order to solve this problems. In this thesis, we explore the effectiveness of Deep Learning techniques in attempting to automatize and improve the diagnosis of atheroma plaques. To achieve so we tackle the following problems: ultrasound image segmentation and plaque tissue classification. The techniques applied in this work are the following. For the segmentation of the common carotid artery IMT we replicate a state of the art Fully Convolutional Network approach and explore the implementation of a trained network to another dataset. Regarding the plaque classification problem, we explore the performance of Convolutional Neural Networks as well with two baseline methods. These techniques are applied on two datasets: REGICOR and NEFRONA. These datasets are provided by two research groups of IMIM and IRBLleida in collaboration in a larger project with the UB. A data exploration analysis is also presented on the patient’s data of NEFRONA to justify the importance of detecting the atherosclerotic plaques and thus the techniques we explore

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Quantifying atherosclerosis in vasculature using ultrasound imaging

Cerebrovascular disease accounts for approximately 30% of the global burden associated with cardiovascular diseases [1]. According to the World Stroke Organisation, there are approximately 13.7 million new stroke cases annually, and just under six million people will die from stroke each year [2]. The underlying cause of this disease is atherosclerosis – a vascular pathology which is characterised by thickening and hardening of blood vessel walls. When fatty substances such as cholesterol accumulate on the inner linings of an artery, they cause a progressive narrowing of the lumen referred to as a stenosis. Localisation and grading of the severity of a stenosis, is important for practitioners to assess the risk of rupture which leads to stroke. Ultrasound imaging is popular for this purpose. It is low cost, non-invasive, and permits a quick assessment of vessel geometry and stenosis by measuring the intima media thickness. Research is showing that 3D monitoring of plaque progression may provide a better indication of sites which are at risk of rupture. Various metrics have been proposed. From these, the quantification of plaques by measuring vessel wall volume (VWV) using the segmented media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, methods to segment these boundaries are required to help generate VWV measurements with high accuracy, less user interaction and increased robustness to variability in di↵erent user acquisition protocols.ii This work proposes three novel methods to address these requirements, to ultimately produce a highly accurate, fully automated segmentation algorithm which works on intensity-invariant data. The first method proposed was that of generating a novel, intensity-invariant representation of ultrasound data by creating phase-congruency maps from raw unprocessed radio-frequency ultrasound information. Experiments carried out showed that this representation retained the necessary anatomical structural information to facilitate segmentation, while concurrently being invariant to changes in amplitude from the user. The second method proposed was the novel application of Deep Convolutional Networks (DCN) to carotid ultrasound images to achieve fully automatic delineation of the MAB boundaries, in addition to the use of a novel fusion of amplitude and phase congruency data as an image source. Experiments carried out showed that the DCN produces highly accurate and automated results, and that the fusion of amplitude and phase yield superior results to either one alone. The third method proposed was a new geometrically constrained objective function for the network's Stochastic Gradient Descent optimisation, thus tuning it to the segmentation problem at hand, while also developing the network further to concurrently delineate both the MAB and LIB to produce vessel wall contours. Experiments carried out here also show that the novel geometric constraints improve the segmentation results on both MAB and LIB contours. In conclusion, the presented work provides significant novel contributions to field of Carotid Ultrasound segmentation, and with future work, this could lead to implementations which facilitate plaque progression analysis for the end�user

Coronary artery properties in atherosclerosis: A deep learning predictive model

In this work an Artificial Neural Network (ANN) was developed to help in the diagnosis of plaque vulnerability by predicting the Young modulus of the core (Ecore) and the plaque (Eplaque) of atherosclerotic coronary arteries. A representative in silico database was constructed to train the ANN using Finite Element simulations covering the ranges of mechanical properties present in the bibliography. A statistical analysis to pre-process the data and determine the most influential variables was performed to select the inputs of the ANN. The ANN was based on Multilayer Perceptron architecture and trained using the developed database, resulting in a Mean Squared Error (MSE) in the loss function under 10–7, enabling accurate predictions on the test dataset for Ecore and Eplaque. Finally, the ANN was applied to estimate the mechanical properties of 10,000 realistic plaques, resulting in relative errors lower than 3%

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors

Gradient free sign activation zero one loss neural networks for adversarially robust classification

The zero-one loss function is less sensitive to outliers than convex surrogate losses such as hinge and cross-entropy. However, as a non-convex function, it has a large number of local minima, andits undifferentiable attribute makes it impossible to use backpropagation, a method widely used in training current state-of-the-art neural networks. When zero-one loss is applied to deep neural networks, the entire training process becomes challenging. On the other hand, a massive non-unique solution probably also brings different decision boundaries when optimizing zero-one loss, making it possible to fight against transferable adversarial examples, which is a common weakness in deep learning neural network models. This dissertation introduces a stochastic coordinate descent to optimize the linear classification model based on zero-one loss. Moreover, its variants are successfully applied to multi-layer neural networks using sign activation and multi-layer convolutional neural networks to obtain higher image classification performance. In some image benchmark tests, the stochastic coordinate descent method achieves accuracy close to that of the stochastic gradient descent method. At the same time, some heuristic techniques are used, such as random node optimization, feature pool, warm start, step training, additional backpropagation penetration, and other methods to speed up training and save memory usage. Furthermore, the model\u27s adversarial robustness is analyzed by conducting white-box attacks, decision boundary attacks, and comparing zero-one loss models to those using more traditional loss functions such as cross-entropy