KNOWLEDGE-GUIDED AND HYPER-ATTENTION AWARE JOINT NETWORK FOR BENIGN-MALIGNANT LUNG NODULE CLASSIFICATION

Accurate identification and early diagnosis of malignant lung nodules are crucial for improving the survival rate of patients with lung cancer. Deep learning methods have recently been proven success in computer-aided diagnostic tasks. However, to the best of our knowledge, the features of tissues and vessels will disturb the model resulting in inaccurate classification of the nodules. To reduce the interference and capture crucial contextual information from different channels in a more efficient way, we introduce a Hyper-Attention Mechanism(HAM) that can be easily integrated into convolutional neural networks(CNNs). Moreover, without incorporating prior-domain knowledge, traditional methods lack interpretability, which is difficult to understand and utilize them in the clinic by radiologists. Based on this, we propose a novel Knowledge-Guided model to predict malignant pulmonary nodules from chest CT data, which inject external medical knowledge into CNNs to guide the training process. We evaluate the proposed model on the LIDC-IDRI dataset and demonstrate its effectiveness by achieving comparable state-of-the-art performance.