Abstract Machine learning (ML) has been increasingly used within cardiology, particularly in
the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML …

The majority of coronary atherothrombotic events presenting as myocardial infarction (MI)
occur as a result of plaque rupture or erosion. Understanding the evolution from a stable …

Anomaly detection with weakly supervised video-level labels is typically formulated as a
multiple instance learning (MIL) problem, in which we aim to identify snippets containing …

Background—Apoptosis is common in advanced human atheroma and contributes to
plaque instability. Because annexin V has a high affinity for exposed phosphatidylserine on …

Objectives: The purpose of this study was to evaluate interstitial alterations in myocardial
remodeling using a radiolabeled Cy5. 5-RGD imaging peptide (CRIP) that targets …

Preclinical and clinical diagnostics increasingly rely on techniques to visualize internal
organs at high resolution via endoscopes. Miniaturized endoscopic probes are necessary …

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal
(ie, healthy) images to detect any abnormal (ie, unhealthy) samples that do not conform to …

Multimodal microendoscopes enable co‐located structural and molecular measurements in
vivo, thus providing useful insights into the pathological changes associated with disease …

Objectives: This study sought to determine whether indocyanine green (ICG)–enhanced
near-infrared fluorescence (NIRF) imaging can illuminate high-risk histologic plaque …

Anomaly detection methods generally target the learning of a normal image distribution (ie,
inliers showing healthy cases) and during testing, samples relatively far from the learned …