Active Semi-Supervised Learning via Bayesian Experimental Design for Lung Cancer Classification Using Low Dose Computed Tomography Scans

• Published in: Applied sciences Vol. 13; no. 6; p. 3752
• Main Authors: Nguyen, Phuong, Rathod, Ankita, Chapman, David, Prathapan, Smriti, Menon, Sumeet, Morris, Michael, Yesha, Yelena
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Active learning; Algorithms; Analysis; Artificial intelligence; Biopsy; Cancer; Classification; computer-aided diagnosis; CT imaging; Data mining; Datasets; Deep learning; Diagnosis; Diagnostic imaging; expectation maximization; Labeling; Lung cancer; lung cancer screening; Medical imaging; Medical imaging equipment; Medical screening; Neural networks; Oncology, Experimental; Radiomics; Semantics; Tomography; Taiwan
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13063752

We introduce an active, semisupervised algorithm that utilizes Bayesian experimental design to address the shortage of annotated images required to train and validate Artificial Intelligence (AI) models for lung cancer screening with computed tomography (CT) scans. Our approach incorporates active learning with semisupervised expectation maximization to emulate the human in the loop for additional ground truth labels to train, evaluate, and update the neural network models. Bayesian experimental design is used to intelligently identify which unlabeled samples need ground truth labels to enhance the model’s performance. We evaluate the proposed Active Semi-supervised Expectation Maximization for Computer aided diagnosis (CAD) tasks (ASEM-CAD) using three public CT scans datasets: the National Lung Screening Trial (NLST), the Lung Image Database Consortium (LIDC), and Kaggle Data Science Bowl 2017 for lung cancer classification using CT scans. ASEM-CAD can accurately classify suspicious lung nodules and lung cancer cases with an area under the curve (AUC) of 0.94 (Kaggle), 0.95 (NLST), and 0.88 (LIDC) with significantly fewer labeled images compared to a fully supervised model. This study addresses one of the significant challenges in early lung cancer screenings using low-dose computed tomography (LDCT) scans and is a valuable contribution towards the development and validation of deep learning algorithms for lung cancer screening and other diagnostic radiology examinations.