MBH-Net: Multi-branch Hybrid Network with Auxiliary Attention Guidance for Large Vessel Occlusion Detection

Acute ischemic stroke (AIS) caused by large vessel occlusion (LVO) has high disability and mortality. However, due to the individual differences of physiological structure and pathological changes between patients, it will be difficult to detect the occluded vessels, so as to delay the treatment timing. Therefore, it is of great significance to a ssist d octors to locate occluded vessels quickly and accurately in clinical practice. In this paper, we present a novel multi-branch hybrid network (MBH-Net) with auxiliary attention guidance to detect occluded vessels. The proposed network consists of three branches for universal representation learning, patient representation learning and classifier learning, respectively. Furthermore, we propose a semantic feature enhancement module to extract more robust semantic information. Particularly, we introduce an auxiliary attention guidance module to guide the attention tendency of MBH-Net, which can make the network give a more reasonable visual interpretation. Extensive experiments show that our MBH-Net can achieve satisfactory accuracy and give a reasonable visual interpretation.