  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.
