Lung Nodule Classification Research Articles

From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology. Modern medical care and imaging technology are becoming increasingly inseparable. However, the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (from signal to image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we develop an AI-based signal-to-knowledge diagnostic scheme for lung nodule classification directly from the computed tomography (CT) raw data (the signal). We find that the raw data achieves almost comparable performance with CT, indicating that it is possible to diagnose diseases without reconstructing images. Moreover, the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts (with a gain ranging from 0.01 to 0.12), demonstrating that raw data contains diagnostic information that CT does not possess. Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

Assessment of artificial intelligence-aided computed tomography in lung cancer screening

BackgroundLung cancer is one of the most common causes of cancer-related deaths in developed and developing countries. Therefore, early detection of lung cancer has a significant impact on lung cancer surveillance. Interpretation of lung CT scans for cancer screening is considered an intensive task for most radiologists, and long experience is required for accurate diagnosis through visual processing. This cross-sectional study introduces automated CAD software (Careline Soft’s AVIEW Metric software). This software can detect and classify lung nodules in CT scans. The performance of a deep learning (DL) model embedded in that software will be compared with that of the radiologists. Also, the feasibility of lung cancer screening protocol is evaluated in Suez Canal University Hospital, Ismailia, Egypt, by implementing Lung Imaging Reporting and Data System (Lung-RADS).ResultsAs for the detection of the pulmonary nodules, the initial review by the CAD system (without validation by the researcher radiologist) has high sensitivity (93.0%) and specificity (95.5%) with overall accuracy of 93.6%. After review of the automatically detected nodules by the researcher radiologist was done, the final CAD has higher sensitivity (98.2%) and comparable specificity (95.5%) for the detection of pulmonary nodules with overall accuracy of 97.4%. As for lung cancer screening (categorization of Lung-RADS 3 and 4 nodules), unrevised initial computer-aided detection has 97.9% specificity and 96.9% for lung cancer screening with overall accuracy of 97.4%. After second look and review of the CAD result by the researcher radiologist, there is total agreement in total number of nodules and categorization of Lung-RADS 3 and 4. This gives an excellent agreement of 88.6% (κ = 0.951) between the CAD system and reference radiologist in the overall categorization of all lung nodules according to Lung-RADS classification.ConclusionsThe application of CAD system demonstrated increased sensitivity and specificity for the detection of lung nodules and total agreement in the detection of suspicious and probably benign nodules (lung cancer screening) and excellent level of agreement in the overall lung nodule categorization (Lung-RADS).

Impact of localized fine tuning in the performance of segmentation and classification of lung nodules from computed tomography scans using deep learning.

Algorithm malfunction may occur when there is a performance mismatch between the dataset with which it was developed and the dataset on which it was deployed. A baseline segmentation algorithm and a baseline classification algorithm were developed using public dataset of Lung Image Database Consortium to detect benign and malignant nodules, and two additional external datasets (i.e., HB and XZ) including 542 cases and 486 cases were involved for the independent validation of these two algorithms. To explore the impact of localized fine tuning on the individual segmentation and classification process, the baseline algorithms were fine tuned with CT scans of HB and XZ datasets, respectively, and the performance of the fine tuned algorithms was tested to compare with the baseline algorithms. The proposed baseline algorithms of both segmentation and classification experienced a drop when directly deployed in external HB and XZ datasets. Comparing with the baseline validation results in nodule segmentation, the fine tuned segmentation algorithm obtained better performance in Dice coefficient, Intersection over Union, and Average Surface Distance in HB dataset (0.593 vs. 0.444; 0.450 vs. 0.348; 0.283 vs. 0.304) and XZ dataset (0.601 vs. 0.486; 0.482 vs. 0.378; 0.225 vs. 0.358). Similarly, comparing with the baseline validation results in benign and malignant nodule classification, the fine tuned classification algorithm had improved area under the receiver operating characteristic curve value, accuracy, and F1 score in HB dataset (0.851 vs. 0.812; 0.813 vs. 0.769; 0.852 vs. 0.822) and XZ dataset (0.724 vs. 0.668; 0.696 vs. 0.617; 0.737 vs. 0.668). The external validation performance of localized fine tuned algorithms outperformed the baseline algorithms in both segmentation process and classification process, which showed that localized fine tuning may be an effective way to enable a baseline algorithm generalize to site-specific use.

An approach for classification of lung nodules

The main objective of the proposed work is to develop an automated computer-aided detection (CAD) system to classify lung nodules using various classifiers from computed tomography (CT) images. One of the most important steps in lung nodule detection is the classification of nodule and non-nodule patterns in CT. The early detection of the condition helps lower the mortality rate. The developed CAD systems consist of segmentation, feature extraction, and classification. In this work, a filter method is used to segment the infected region. Later, we extracted features through and fed into classifiers such as Decision Stump (DS), Random Forest (RF), and Back Propagation Neural Network (BPNN). The experimentation was conducted on LIDC-IDRI dataset, and the results with BPNN outperformed those with DS and RF classifiers.

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

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 multi-view (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. 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. 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. 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.

Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT.

To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up. A machine learning classifier was developed and trained using imaging data from the National Lung Screening Trial (NSLT) to produce a malignancy risk score (malignancy Similarity Index [mSI]) for individual lung nodules. In addition to NLST cohorts, external cohorts were developed from a tertiary referral lung cancer screening program data set and an external nonscreening data set of all nodules detected on CT. Performance of the mSI combined with Lung-RADS was compared with Lung-RADS alone and the Mayo and Brock risk calculators. We analyzed 963 subjects and 1,331 nodules across these cohorts. The mSI was comparable in accuracy (area under the curve= 0.89) to existing clinical risk models (area under the curve= 0.86-0.88) and independently predictive in the NLST cohort of 704 nodules. When compared with Lung-RADS, the mSI significantly increased sensitivity across all cohorts (25%-117%), with significant increases in specificity in the screening cohorts (17%-33%). When used in conjunction with Lung-RADS, use of mSI would result in earlier diagnoses and reduced follow-up across cohorts, including the potential for early diagnosis in 42% of malignant NLST nodules from prior-year CT scans. A computer-assisted diagnosis software improved risk classification from chest CTs of screening and incidentally detected lung nodules compared with Lung-RADS. mSI added predictive value independent of existing radiological and clinical variables. These results suggest the generalizability and potential clinical impact of a tool that is straightforward to implement in practice.

An attention-based deep learning network for lung nodule malignancy discrimination.

Effective classification of lung cancers plays a vital role in lung tumor diagnosis and subsequent treatments. However, classification of benign and malignant lung nodules remains inaccurate. This study proposes a novel multimodal attention-based 3D convolutional neural network (CNN) which combines computed tomography (CT) imaging features and clinical information to classify benign and malignant nodules. An average diagnostic sensitivity of 96.2% for malignant nodules and an average accuracy of 81.6% for classification of benign and malignant nodules were achieved in our algorithm, exceeding results achieved from traditional ResNet network (sensitivity of 89% and accuracy of 80%) and VGG network (sensitivity of 78% and accuracy of 73.1%). The proposed deep learning (DL) model could effectively distinguish benign and malignant nodules with higher precision.

3D SAACNet with GBM for the classification of benign and malignant lung nodules

In view of the low diagnostic accuracy of the current classification methods of benign and malignant pulmonary nodules, this paper proposes a 3D segmentation attention network integrating asymmetric convolution (SAACNet) classification model combined with a gradient boosting machine (GBM). This can make full use of the spatial information of pulmonary nodules. First, the asymmetric convolution (AC) designed in SAACNet can not only strengthen feature extraction but also improve the network's robustness to object flip and rotation detection and improve network performance. Second, the segmentation attention network integrating AC (SAAC) block can effectively extract more fine-grained multiscale spatial information while adaptively recalibrating multidimensional channel attention weights. The SAACNet also uses a dual-path connection for feature reuse, where the model makes full use of features. In addition, this article makes the loss function pay more attention to difficult and misclassified samples by adding adjustment factors. Third, the GBM is used to splice the nodule size, originally cropped nodule pixels, and the depth features learned by SAACNet to improve the prediction accuracy of the overall model. A comprehensive ablation experiment is carried out on the public dataset LUNA16 and compared with other lung nodule classification models. The classification accuracy (ACC) is 95.18%, and the area under the curve (AUC) is 0.977. The results show that this method effectively improves the classification performance of pulmonary nodules. The proposed method has advantages in the classification of benign and malignant pulmonary nodules, and it can effectively assist radiologists in pulmonary nodule classification.

Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns. In this work, an Auxiliary Classifier (AC)-Generative Adversarial Network (GAN) based Lung Cancer Classification (LCC) system is developed. The proposed AC-GAN-LCC system consists of three modules; preprocessing, Lungs Region Detection (LRD), and AC-GAN classification. A Wiener filter is employed in the preprocessing module to remove the Gaussian noise. In the LRD module, only the lung regions (left and right lungs) are detected using iterative thresholding and morphological operations. In order to extract the lung region only, flood filling and background subtraction. The detected lung regions are fed to the AC-GAN classifier to detect the nodules. It classifies the nodules into one of the two classes, i.e., binary classification (such as nodules or non-nodules). The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image. Three different optimization techniques, adaptive gradient optimization, root mean square propagation optimization, and Adam optimization are employed for optimizing the AC-GAN architecture. The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium (LIDC) database Computed Tomography (CT) scan images. The proposed AC-GAN-LCC system classifies ∼15000 CT slices (7310 non-nodules and 7685 nodules). It provides an overall accuracy of 98.8% on the LIDC database using Adam optimization by a 10-fold cross-validation approach.

Multi-Orientation Local Texture Features for Guided Attention-Based Fusion in Lung Nodule Classification

Computerized tomography (CT) scan images are widely used in automatic lung cancer detection and classification. The lung nodules’ texture distribution throughout the CT scan volume can vary significantly, and accurate identification and consideration of discriminative information in this volume can greatly help the classification process. Deep stacks of recurrent and convolutional operations cannot entirely represent such variations, especially in the size and location of the nodules. To model this complex pattern of inter/intra dependencies in the CT slices of each nodule, a multi-orientation-based guided-attention module (MOGAM) is proposed in this paper, which provides high flexibility in concentrating on the relevant information extracted from different regions of the nodule in a non-local manner. Moreover, to provide the model with finer-grained discriminative information from the nodule volume, specifically-designed local texture feature descriptors (TFDs) are extracted from the nodule slices in multiple orientations. These TFDs not only represent the distribution of textural information across multiple slices of a nodule but also encode and approximate this distribution within each slice. The extended experimentation has shown the effectiveness of the non-local combination of these local TFDs through the proposed guided attention mechanism. According to the classification results obtained on the standard LIDC-IDRI dataset, the proposed approach has outperformed other counterparts in terms of accuracy and AUC evaluation metrics. Also, a detailed explainability analysis of the results is provided, demonstrating the correct functioning of the proposed attention-based fusion approach, which is required by medical experts.

Trustworthy learning with (un)sure annotation for lung nodule diagnosis with CT.

Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two generalizability issues in clinical practice. First, benign-malignant discrimination is often assessed by human observers without pathologic diagnoses at the nodule level. We termed these data as "unsure-annotation data". Second, a classifier does not necessarily acquire reliable nodule features for stable learning and robust prediction with patch-level labels during learning. In this study, we construct a sure-annotation dataset with pathologically-confirmed labels and propose a collaborative learning framework to facilitate sure nodule classification by integrating unsure-annotation data knowledge through nodule segmentation and malignancy score regression. A loss function is designed to learn reliable features by introducing interpretability constraints regulated with nodule segmentation maps. Furthermore, based on model inference results that reflect the understanding from both machine and experts, we explore a new nodule analysis method for similar historical nodule retrieval and interpretable diagnosis. Detailed experimental results demonstrate that our approach is beneficial for achieving improved performance coupled with trustworthy model reasoning for lung cancer prediction with limited data. Extensive cross-evaluation results further illustrate the effect of unsure-annotation data for deep-learning based methods in lung nodule classification.

Pulmonary Lung Nodule Detection and classification through Image Enhancement and Deep Learning

Pulmonary Lung Nodule Detection and classification through Image Enhancement and Deep Learning

Pulmonary lung nodule detection and classification through image enhancement and deep learning

Pulmonary lung nodule detection and classification through image enhancement and deep learning

Multi-Label Softmax Networks for Pulmonary Nodule Classification Using Unbalanced and Dependent Categories.

Radiographic attributes of lung nodules remedy the shortcomings of lung cancer computer-assisted diagnosis systems, which provides interpretable diagnostic reference for doctors. However, current studies fail to dedicate multi-label classification of lung nodules using convolutional neural networks (CNNs) and are inferior in exploiting statistical dependency between the labels. In addition, data imbalance is an indispensable problem to be reckoned with when employing CNNs to perform lung nodule classification. It introduces greater challenges especially in the multi-label classification. In this paper, we propose a method called MLSL-Net to discriminate lung nodule characteristics and simultaneously address the challenges. Particularly, the proposal employs multi-label softmax loss (MLSL) as the performance index, aiming to reduce the ranking errors between the labels and within the labels during training, thereby optimizing ranking loss and AUC directly. Such criterions can better evaluate the classifier's performance on the multi-label imbalanced dataset. Furthermore, a scale factor is introduced based on the investigation of the max surrogate function. Different from preceding usages, the small factor is used so that to narrow the discrepancy of gradients produced by different labels. More interestingly, this factor also facilitates the exploit of label dependency. Experimental results on the LIDC-IDRI dataset as well as another akin dataset demonstrate that MLSL-Net can effectively perform multi-label classification despite the imbalance issue. Meanwhile, the results confirm the responsibility of the factor for capturing label correlations, accordingly leading to more accurate predictions.

Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme

Intelligent lung nodules classification is a meaningful and challenging research topic for early precaution of lung cancers, which aims to diagnose the malignancy of candidate nodules from the pulmonary computed tomography images. Nowadays, deep learning methods have made significant achievements in the medical field and promoted developments of lung nodules classification. Nevertheless, mainstream CNNs-based networks typically excel in learning coarse-grained local feature representations via stacked local-aware and weight-shared convolutions, and cannot practically model the long-range context interaction and the spatial dependencies. To tackle the above difficulties, we innovatively propose an effective Multi-Granularity Dilated Transformer to learn the long-range context relations, and explore fine-grained local details via the proposed Local Focus Scheme. Specifically, we delicately design a novel Deformable Dilated Transformer to incorporate diverse contextual information with self-attention for learning long-range global spatial dependencies. Moreover, numerous investigations indicate that local details are extremely crucial to classify indistinguishable lung nodules. Thus, we propose the Local Focus Scheme to focus on the more discriminative local features by modeling channel-wise grouped topology. Consequently, the Multi-Granularity Dilated Transformer is constructed by leveraging the Local Focus Scheme to guide the Deformable Dilated Transformer for learning fine-grained local cues. Experimental results on the mainstream benchmark LIDC-IDRI demonstrate the superiority of our model compared with the state-of-the-art methods.

Atrous convolution aided integrated framework for lung nodule segmentation and classification

Lung cancer has been recognized as the most life-threatening cancer all over the world. Appropriate detection of lung nodule using Computed Tomography (CT) images helps in early stage recognition of lung cancer. Different computer-aided algorithms play an important role in the early diagnosis of lung cancer and can increase the five-year survival rate of lung cancer patients. However, due to structural similarity, manually recognizing the malignant nodule from the benign is time-consuming and challenging task. Recently different deep learning (DL) based Computer-aided diagnosis (CADx) systems have been developed for lung nodule characterization. In this work, an integrated nodule segmentation and characterization framework has been developed using the concept of atrous convolution. The proposed Atrous Convolution-based Convolutional Neural Network (ATCNN) framework can segment and characterize lung nodules by capturing multi-scale features from the HRCT images. Different variants of the ATCNN framework have been analyzed for lung nodule characterization. Among them, ATCNN with a two-layer atrous pyramid and residual connections (ATCNN2PR) has demonstrated the highest classification performance indices for nodule characterization. The new ATCNN2PR framework has obtained an average Dice Similarity Coefficient (DSC), Jaccard Index (JI), and Boundary F1 (BF) score of 0.9715, 0.9520, and 0.9584 for nodule segmentation and sensitivity, specificity, accuracy of 95.84%, 96.89%, and 95.97% for lung nodule characterization on LIDC-IDRI dataset. The proposed automatic trainable end-to-end system has outperforms other competing frameworks by capturing multi-scale features from High-Resolution Computed Tomography (HRCT) nodule images.

BCDU-Net and chronological-AVO based ensemble learning for lung nodule segmentation and classification

ABSTRACT Lung cancer is the deadly disorder occurring worldwide among all age groups and it causes high mortality rate. Traditionally, Computed Tomography (CT) is employed as a non-invasive tool to detect the position of tumour in the lung and its severity level. It is quintessential to detect the nodules concealed in the lung that is mainly responsible for deadly cancer disease. Lung nodule segmentation has been recognised as a significant topic for research discussion among researchers and major obstacle faced in this lung nodule segmentation is accuracy, which is mainly deteriorated because of the visual variations and diversity in lung nodules. Therefore, to enhance the segmentation performance level of lung nodule detection, this research proposes an effective strategy for lung nodule segmentation and classification using newly devised method named Chronological-African Vultures Optimisation (CAVO) algorithm. Here, lung nodule segmentation is carried out using Bi-directional ConvLSTM-U-Net (BCDU-Net) and lung nodule classification is done using ensemble classifier, such as Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Deep Residual Network (DRN). Meanwhile, the proposed CAVO-based ensemble learning has provided better insights with high testing accuracy, maximum sensitivity, and maximum specificity with measures of 0.946, 0.930, and 0.890 using dataset-1.

Accurate classification of lung nodules on CT images using the TransUnet.

Computed tomography (CT) is an effective way to scan for lung cancer. The classification of lung nodules in CT screening is completely doctor dependent, which has drawbacks, including difficulty classifying tiny nodules, subjectivity, and high false-positive rates. In recent years, deep convolutional neural networks, a deep learning technology, have been shown to be effective in medical imaging diagnosis. Herein, we propose a deep convolutional neural network technique (TransUnet) to automatically classify lung nodules accurately. TransUnet consists of three parts: the transformer, the Unet, and global average pooling (GAP). The transformer encodes discriminative features via global self-attention modeling on CT image patches. The Unet, which collects context by constricting route, enables exact lunge nodule localization. The GAP categorizes CT images, assigning each sample a score. Python was employed to pre-process all CT images in the LIDI-IDRI, and the obtained 8,474 images (3,259 benign and 5,215 lung nodules) were used to evaluate the method's performance. The accuracies of TransUnet in the training and testing sets were 87.90 and 84.62%. The sensitivity, specificity, and AUC of the proposed TransUnet on the testing dataset were 70.92, 93.17, and 0.862%, respectively (0.844-0.879). We also compared TransUnet to three well-known methods, which outperformed these methods. The experimental results on LIDI-IDRI demonstrated that the proposed TransUnet has a great performance in classifying lung nodules and has a great potential application in diagnosing lung cancer.

An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification

Background and ObjectiveLung cancer has the highest cancer-related mortality worldwide, and lung nodule usually presents with no symptom. Low-dose computed tomography (LDCT) was an important tool for lung cancer detection and diagnosis. It provided a complete three-dimensional (3-D) chest image with a high resolution.Recently, convolutional neural network (CNN) had flourished and been proven the CNN-based computer-aided diagnosis (CADx) system could extract the features and help radiologists to make a preliminary diagnosis. Therefore, a 3-D ResNeXt-based CADx system was proposed to assist radiologists for diagnosis in this study. MethodsThe proposed CADx system consists of image preprocessing and a 3-D CNN-based classification model for pulmonary nodule classification. First, the image preprocessing was executed to generate the normalized volumn of interest (VOI) only including nodule information and a few surrounding tissues. Then, the extracted VOI was forwarded to the 3-D nodule classification model. In the classification model, the RestNext was employed as the backbone and the attention scheme was embedded to focus on the important features. Moreover, a multi-level feature fusion network incorporating feature information of different scales was used to enhance the prediction accuracy of small malignant nodules. Finally, a hybrid loss based on channel optimization which make the network learn more detailed information was empolyed to replace a binary cross-entropy (BCE) loss. ResultsIn this research, there were a total of 880 low-dose CT images including 440 benign and 440 malignant nodules from the American National Lung Screening Trial (NLST) for system evaluation. The results showed that our system could achieve the accuracy of 85.3%, the sensitivity of 86.8%, the specificity of 83.9%, and the area-under-curve (AUC) value was 0.9042. It was confirmed that the designed system had a good diagnostic ability. ConclusionIn this study, a CADx composed of the image preprocessing and a 3-D nodule classification model with attention scheme, feature fusion, and hybrid loss was proposed for pulmonary nodule classification in LDCT. The results indicated that the proposed CADx system had potential for achieving high performance in classifying lung nodules as benign and malignant.

Self-supervised transfer learning framework driven by visual attention for benign–malignant lung nodule classification on chest CT

Lung cancer is one of the most fatal malignant diseases, which poses an acute menace to human health and life. The accurate differential diagnosis of lung nodules is a vital step in the computed tomography (CT)-based noninvasive screening of lung cancer. Though deep learning-based methodologies have achieved good results in the task of nodule malignancy prediction, there are still two fundamental challenges that are required to be overcome, including insufficient labeled samples and the interferences of background tissues. Motivated by the above facts, a self-supervised transfer learning framework driven by visual attention (STLF-VA) is presented for benign–malignant identification of nodules on chest CT, which advocates using volumes containing the entire nodule objects as inputs to obtain discriminative features. Compared with traditional models that designed 2D natural image-based transfer learning models or learning from scratch 3D models, the proposed STLF-VA method can effectively alleviate the dependence on labeled samples by exploring the valuable information from 3D unlabeled CT scans in a coarse-to-fine self-supervised transfer learning fashion. Unlike the single attention mechanism, the multi-view aggregative attention (MVAA) module embedded in the STLF-VA architecture fully recalibrates the multi-layer feature maps from multiple attention angles, and can strengthen the anti-interference ability on background information. Moreover, a new dataset CQUCH-LND is constructed for evaluating the effectiveness of the STLF-VA model in clinical practice. Experimental results on the clinical dataset CQUCH-LND and the public dataset LIDC-IDRI indicate that the proposed STLF-VA framework achieves more competitive performance than some state-of-the-art nodule classification approaches.