Work

Computer-aided classification of lung nodules on CT images with expert knowledge

Year: 2021

Type: conference-paper

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.

Source: SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling

DOI: 10.1117/12.2581888

Authors: Chuangye Wan, Ling Ma, Xiabi Liu, Baowei Fei

Institutions: Nankai University, Beijing Institute of Technology, The University of Texas at Dallas, The University of Texas Southwestern Medical Center

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Heart, Lung, and Blood Institute, National Cancer Institute