TY - GEN
T1 - An Artificial Intelligence Representation of Human Knowledge for Lung Nodule Classification
AU - Gao, Yongfeng
AU - Pomeroy, Marc
AU - Cao, Weiguo
AU - Han, Fangfang
AU - Liang, Zhengrong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Low-dose computed tomography (LdCT), a recommended screening method for detection of early lung cancer, has high false positive (FP) rate, and lung nodule biopsy is a follow-up option to eliminate the FPs. It is challenging and meaningful to differentiate the nodule pathology by the LdCT screening data to avoid the costly interventional biopsy procedure. In this paper, we propose an artificial intelligence (AI) model to represent the human knowledge about lesion properties to differentiate the malignant nodules from benign ones. Three lesion properties in terms of heterogeneity, elasticity and growth are quantitatively represented by the proposed AI model. An augmented feature selection strategy was developed to integrate all lesion properties for the lesion classification. Experimental results show that the proposed AI model can achieve an AUC (area under the curve of receiver operating characteristics) score of 0.78 in the cases where physicians cannot determine the lesion type with AUC score around 0.5.
AB - Low-dose computed tomography (LdCT), a recommended screening method for detection of early lung cancer, has high false positive (FP) rate, and lung nodule biopsy is a follow-up option to eliminate the FPs. It is challenging and meaningful to differentiate the nodule pathology by the LdCT screening data to avoid the costly interventional biopsy procedure. In this paper, we propose an artificial intelligence (AI) model to represent the human knowledge about lesion properties to differentiate the malignant nodules from benign ones. Three lesion properties in terms of heterogeneity, elasticity and growth are quantitatively represented by the proposed AI model. An augmented feature selection strategy was developed to integrate all lesion properties for the lesion classification. Experimental results show that the proposed AI model can achieve an AUC (area under the curve of receiver operating characteristics) score of 0.78 in the cases where physicians cannot determine the lesion type with AUC score around 0.5.
UR - https://www.scopus.com/pages/publications/85185386765
U2 - 10.1109/NSS/MIC44845.2022.10398909
DO - 10.1109/NSS/MIC44845.2022.10398909
M3 - Conference contribution
AN - SCOPUS:85185386765
T3 - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
BT - 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022
Y2 - 5 November 2022 through 12 November 2022
ER -