Detection of thin‐cap fibroatheroma in IVOCT images based on weakly supervised learning and domain knowledge

Abstract

Automatic detection of thin‐cap fibroatheroma (TCFA) on intravascular optical coherence tomography images is essential for the prevention of acute coronary syndrome. However, existing methods need to mark the exact location of TCFAs on each frame as supervision, which is extremely time‐consuming and expensive. Hence, a new weakly supervised framework is proposed to detect TCFAs using only image‐level tags as supervision. The framework comprises cut, feature extraction, relation, and detection modules. First, based on prior knowledge, a cut module was designed to generate a small number of specific region proposals. Then, to learn global information, a relation module was designed to learn the spatial adjacency and order relationships at the feature level, and an attention‐based strategy was introduced in the detection module to effectively aggregate the classification results of region proposals as the image‐level predicted score. The results demonstrate that the proposed method surpassed the state‐of‐the‐art weakly supervised detection methods.

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

“…As an illustration, the automatic detection of TCFA from each B-scan through an IV-OCT pullback is rendered in a 3D map, with the cap thickness information depicted in Figure 6. In cases where supervised training with extensive labeled datasets is unfeasible, weak supervised learning methods have been proposed for TCFA detection [88]. These studies achieved high sensitivities and specificities for plaque classification, and demonstrated excellent reproducibility and efficient clinical analyses.…”