Related papers: Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images

Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images

URL: http://arxiv.org/abs/2202.02428v1
Date: Fri, 4 Feb 2022 23:10:24 GMT
Title: Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images
Authors: Theofanis Ganitidis, Maria Athanasiou, Kalliopi Dalakleidi, Nikos Melanitis, Spyretta Golemati, Konstantina S Nikita
Abstract summary: Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually. This paper introduces an interpretable classification approach of carotid ultrasound images for the risk assessment and stratification of patients with carotid atheromatous plaque.
Score: 1.1254693939127909
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually. Early diagnosis of such cases is of great importance, since it enables clinicians to apply a more effective treatment strategy. This paper introduces an interpretable classification approach of carotid ultrasound images for the risk assessment and stratification of patients with carotid atheromatous plaque. To address the highly imbalanced distribution of patients between the symptomatic and asymptomatic classes (16 vs 58, respectively), an ensemble learning scheme based on a sub-sampling approach was applied along with a two-phase, cost-sensitive strategy of learning, that uses the original and a resampled data set. Convolutional Neural Networks (CNNs) were utilized for building the primary models of the ensemble. A six-layer deep CNN was used to automatically extract features from the images, followed by a classification stage of two fully connected layers. The obtained results (Area Under the ROC Curve (AUC): 73%, sensitivity: 75%, specificity: 70%) indicate that the proposed approach achieved acceptable discrimination performance. Finally, interpretability methods were applied on the model's predictions in order to reveal insights on the model's decision process as well as to enable the identification of novel image biomarkers for the stratification of patients with carotid atheromatous plaque.Clinical Relevance-The integration of interpretability methods with deep learning strategies can facilitate the identification of novel ultrasound image biomarkers for the stratification of patients with carotid atheromatous plaque.

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