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

Segmentation of atherosclerotic carotid plaque in ultrasound video

The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the arterial wall including plaque size, composition and elasticity represent important predictors used in the assessment of the risk for future cardiovascular events. This paper proposes and evaluates an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound video of the CCA based on normalization, speckle reduction filtering (with the hybrid median filter) and parametric active contours. The algorithm is initialized in the first video frame of the cardiac cycle with human assistance and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. The algorithm is evaluated on 10 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, for every 20 frames in a time span of 3-5 seconds, covering in general 2 cardiac cycles. The segmentation results are very satisfactory with a true negative fraction (TNF) of 79.3%, a true-positive fraction (TPF) of 78.12%, a false-positive fraction (FPF) of 6.7% and a false-negative fraction (FNF) of 19.6% between the ground truth and the presented plaque segmentations, a Williams index (KI) of 80.3%, an overlap index of 71.5%, a specificity of 0.88±0.09, a precision of 0.86±0.10 and an effectiveness measure of 0.77±0.09. The results show that integrated system investigated in this study could be successfully used for the automated video segmentation of the carotid plaque