@inproceedings{160f07e9464242bfa9655cd3fa431099,
title = "Computer-aided classification of lung nodules on CT images with expert knowledge",
abstract = "Accurate classification of pulmonary nodules in the CT images is critical for early detection of lung cancer as well as the assessment of the effect from COVID-19. In this paper, we propose a computer-aided classification method for lung nodules using expert knowledge. We use a decoupling metric learning model to describe the deep characteristics of the nodules and then calculate the similarity between the current nodule and the nodules in the database. By analyzing the returned nodules with the diagnosis information, we obtain the expert knowledge of similar nodules, based on which we make the decision of the current nodule. The proposed method has been evaluated on the benchmark LIDC-IDRI dataset and achieved an accuracy of 95.7% and AUC of 0.9901. The proposed classification method can have a variety of applications in lung cancer detection, diagnosis and therapy.",
keywords = "CT, Classification, Convolutional neural networks (CNN), Expert knowledge, Lung nodule",
author = "Chuangye Wan and Ling Ma and Xiabi Liu and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2581888",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Linte, {Cristian A.} and Siewerdsen, {Jeffrey H.}",
booktitle = "Medical Imaging 2021",
address = "United States",
}
