Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data

Phuong Nguyen; David Chapman; Sumeet Menon; Michael Morris; Yelena Yesha

doi:10.1117/12.2549655

16 March 2020 Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data

Phuong Nguyen, David Chapman, Sumeet Menon, Michael Morris, Yelena Yesha

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113142E (2020) https://doi.org/10.1117/12.2549655
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Artificial intelligence (AI) has great potential in medical imaging to augment the clinician as a virtual radiology assistant (vRA) through enriching information and providing clinical decision support. Deep learning is a type of AI that has shown promise in performance for Computer Aided Diagnosis (CAD) tasks. A current barrier to implementing deep learning for clinical CAD tasks in radiology is that it requires a training set to be representative and as large as possible in order to generalize appropriately and achieve high accuracy predictions. There is a lack of available, reliable, discretized and annotated labels for computer vision research in radiology despite the abundance of diagnostic imaging examinations performed in routine clinical practice. Furthermore, the process to create reliable labels is tedious, time consuming and requires expertise in clinical radiology. We present an Active Semi-supervised Expectation Maximization (ASEM) learning model for training a Convolutional Neural Network (CNN) for lung cancer screening using Computed Tomography (CT) imaging examinations. Our learning model is novel since it combines Semi-supervised learning via the Expectation-Maximization (EM) algorithm with Active learning via Bayesian experimental design for use with 3D CNNs for lung cancer screening. ASEM simultaneously infers image labels as a latent variable, while predicting which images, if additionally labeled, are likely to improve classification accuracy. The performance of this model has been evaluated using three publicly available chest CT datasets: Kaggle2017, NLST, and LIDC-IDRI. Our experiments showed that ASEM-CAD can identify suspicious lung nodules and detect lung cancer cases with an accuracy of 92% (Kaggle17), 93% (NLST), and 73% (LIDC) and Area Under Curve (AUC) of 0.94 (Kaggle), 0.88 (NLST), and 0.81 (LIDC). These performance numbers are comparable to fully supervised training, but use only slightly more than 50% of the training data labels .

Citation Download Citation

Phuong Nguyen, David Chapman, Sumeet Menon, Michael Morris, and Yelena Yesha "Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142E (16 March 2020); https://doi.org/10.1117/12.2549655

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