Weakly Supervised Vulnerable Plaques Detection by Ivoct Image

Vulnerable plaque is a major factor leading to the onset of acute coronary syndrome (ACS), and accordingly, the detection of vulnerable plaques (VPs) could guide cardiologists to provide appropriate surgical treatments before the occurrence of an event. In general, hundreds of images are acquired for each patient during surgery. Hence a fast and accurate automatic detection algorithm is needed. However, VPs? detection requires extensive annotation of lesion?s boundary by an expert practitioner, unlike diagnoses. Therefore in this paper, a multiple instances learning-based method is proposed to locate VPs with the image-level labels only. In the proposed method, the clip proposal module, the feature extraction module, and the detection module are integrated to recognize VPs and detect the lesion area. Finally, experiments are performed on the 2017 IVOCT dataset to examine the task of weakly supervised detection of VPs. Although the bounding box of VPs is not used, the proposed method yields comparable performance with supervised learning methods.