Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images

Journal Article · Wed Feb 26 00:00:00 EST 2020 · Medical Physics

DOI:https://doi.org/10.1002/mp.14068· OSTI ID:1601916

Wang, Jun ^[1]; Chen, Xiaorong ^[2]; Lu, Hongbing ^[3]; Zhang, Lichi ^[1]; Pan, Jianfeng ^[2]; Bao, Yong ^[4]; Su, Jiner ^[2]; Qian, Dahong ^[1]

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1. School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200240 China
2. Medical Imaging Department Jinhua Municipal Central Hospital Jinhua 321001 China
3. College of Computer Science and Technology Zhejiang University Hangzhou 310027 China
4. Changzhou Industrial Technology Research Institute of Zhejiang University Changzhou 213022 China

Purpose In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). The automatic determination of a nodule's invasiveness based on chest CT scans can guide treatment planning. However, it is challenging, owing to the insufficiency of training data and their interclass similarity and intraclass variation. To address these challenges, we propose a two‐stage deep learning strategy for this task: prior‐feature learning followed by adaptive‐boost deep learning. Methods The adaptive‐boost deep learning is proposed to train a strong classifier for invasiveness classification of subsolid nodules in chest CT images, using multiple 3D convolutional neural network (CNN)‐based weak classifiers. Because ensembles of multiple deep 3D CNN models have a huge number of parameters and require large computing resources along with more training and testing time, the prior‐feature learning is proposed to reduce the computations by sharing the CNN layers between all weak classifiers. Using this strategy, all weak classifiers can be integrated into a single network. Results Tenfold cross validation of binary classification was conducted on a total of 1357 nodules, including 765 noninvasive (AAH and AIS) and 592 invasive nodules (MIA and IAC). Ablation experimental results indicated that the proposed binary classifier achieved an accuracy of with an AUC of 81.3 . These results are superior compared to those achieved by three experienced chest imaging specialists who achieved an accuracy of , , and , respectively. About 200 additional nodules were also collected. These nodules covered 50 cases for each category (AAH, AIS, MIA, and IAC, respectively). Both binary and multiple classifications were performed on these data and the results demonstrated that the proposed method definitely achieves better performance than the performance achieved by nonensemble deep learning methods. Conclusions It can be concluded that the proposed adaptive‐boost deep learning can significantly improve the performance of invasiveness classification of pulmonary subsolid nodules in CT images, while the prior‐feature learning can significantly reduce the total size of deep models. The promising results on clinical data show that the trained models can be used as an effective lung cancer screening tool in hospitals. Moreover, the proposed strategy can be easily extended to other similar classification tasks in 3D medical images.

• Sponsoring Organization: USDOE
• OSTI ID: 1601916
• Journal Information: Medical Physics, Journal Name: Medical Physics Vol. 47 Journal Issue: 4; ISSN 0094-2405
• Publisher: Wiley Blackwell (John Wiley & Sons)
• Country of Publication: United States
• Language: English