论文部分内容阅读
The availability of pulmonary nodules in CT scan image of lung does not completely specify cancer.The noise in an image and morphology of nodules,like shape and size has an implicit and complex association with cancer,and thus,a careful analysis should be mandatory on every suspected nod-ules and the combination of information of every nodule.In this paper,we introduce a“denoising first”two-path convolutional neural network(DFD-Net)to address this complexity.The in-troduced model is composed of denoising and detection part in an end to end manner.First,a residual learning denoising model(DR-Net)is employed to remove noise during the pre-processing stage.Then,a two-path convolutional neural net-work which takes the denoised image by DR-Net as an input to detect lung cancer is employed.The two paths focus on the joint integration of local and global features.To this end,each path employs different receptive field size which aids to model local and global dependencies.To further polish our model perfor-mance,in different way from the conventional feature concate-nation approaches which directly concatenate two sets of fea-tures from different CNN layers,we introduce discriminant cor-relation analysis to concatenate more representative features.Finally,we also propose a retraining technique that allows us to overcome difficulties associated to the image labels imbalance.We found that this type of model easily first reduce noise in an image,balances the receptive field size effect,affords more rep-resentative features,and easily adaptable to the inconsistency among nodule shape and size.Our intensive experimental re-sults achieved competitive results.