False Positive Reduction of Pulmonary Nodules Based on Mixed Attentional Mechanism

In order to solve the problem of high false positives in the candidate detection stage of pulmonary nodules CAD system, this paper proposes a method to reduce false positives of pulmonary nodules based on mixed attention mechanism. The method can be used as an alternative to the most commonly used 3D CNN classification model at the stage of false positive reduction. It can effectively avoid the problems of large number of parameters and computation in 3D CNN model. In this method, the 3D candidate nodule data is viewed as a slice sequence, and the temporal segment networks model is used in combination with the improved 2D ResNet-18 backbone network which contains mixed attention modules. On the basis of using 2D CNN, the spatial and temporal characteristics of the 3D slice data are effectively studied. Compared with the 3D CNN structure model for pulmonary nodules classification, the method proposed in this paper not only improves the accuracy of nodules classification but also reduces the number of model parameters and the inference time.