3D multi‐view squeeze‐and‐excitation convolutional neural network for lung nodule classification

Abstract

Early screening is crucial to improve the survival rate and recovery rate of lung cancer patients. Computer-aided diagnosis system (CAD) is a powerful tool to assist clinicians in early diagnosis. Lung nodules are characterized by spatial heterogeneity.However,many attempts use the two-dimensional multiview (MV) framework to learn and simply integrate multiple view features. These methods suffer from the problems of not capturing the spatial characteristics effectively and ignoring the variability of multiple views. In this paper, we propose a three-dimensional MV convolutional neural network (3D MVCNN) framework and embed the squeeze-and-excitation (SE) module in it to further address the variability of each view in the MV framework. Methods: First, the 3D multiple view samples of lung nodules are extracted by the spatial sampling method, and a 3D CNN is established to extract 3D abstract features. Second, build a 3D MVCNN framework according to the 3D multiple view samples and 3D CNN. This framework can learn more features of different views of lung nodules, taking into account the characteristics of spatial heterogeneity of lung nodules. Finally, to further address the variability of each view in the MV framework, a 3D MVSECNN model is constructed by introducing a SE module in the feature fusion stage. For training and testing purposes we used independent subsets of the public LIDC-IDRI dataset. Results: For the LIDC-IDRI dataset, this study achieved 96.04% accuracy and 98.59% sensitivity in the binary classification, and 87.76% accuracy in the ternary classification, which was higher than other state-of -the-art studies. The consistency score of 0.948 between the model predictions and pathological diagnosis was significantly higher than that between the clinician's annotations and pathological diagnosis. Conclusions:The results show that our proposed method can effectively learn the spatial heterogeneity of nodules and solve the problem of multiple view variability. Moreover, the consistency analysis indicates that our method can provide clinicians with more accurate results of benign-malignant lung nodule classification for auxiliary diagnosis, which is important for assisting clinicians in clinical diagnosis.

Research in the application of artificial intelligence to lung cancer diagnosis

“…Importantly, the sensitivity is higher. Yang et al (19) used a squeeze excitation module for feature fusion, to solve the problem posed by the different views of a multi-view model; they constructed a 3D multi-view squeeze excitation CNN. The accuracy and sensitivity in terms of classifying benign and malignant pulmonary nodules were 96.04 and 98.59%, respectively; 94.8% of agreement with pathological diagnoses was higher than the agreement achieved by other methods.…”

Robust explanation supervision for false positive reduction in pulmonary nodule detection

“…The complexity of nodule formation increases the workload of radiologists and the uncertainty of human factors to miss the diagnosis of lung cancerous nodules at the early stage. 3 Recently, deep learning (DL) algorithms have shown great success in medical applications regarding classification [4][5][6] and segmentation. [7][8][9] This work aims to propose a framework that leverages the state-of -the-art explainable AI methods 10,11 to increase the accuracy of pulmonary nodule detection.…”

Explainable AI for lung nodule detection and classification in CT images

“…Explainable AI [5][6][7][8][9] integrates model interpretability into deep learning (DL) models [10][11][12] and emerges as a promising avenue for creating interpretable computer-aided detection and diagnosis (CAD) systems [13][14][15][16]. One prevalent explanation tool involves visualizing the model's focus, generating an attention map highlighting the input elements influencing the model's prediction.…”