Parameterized Gompertz-Guided Morphological AutoEncoder for Predicting Pulmonary Nodule Growth

The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLS...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 42; H. 12; S. 3602 - 3613
Hauptverfasser:	Fang, Jiansheng, Wang, Jingwen, Li, Anwei, Yan, Yuguang, Liu, Hongbo, Li, Jiajian, Yang, Huifang, Hou, Yonghe, Yang, Xuening, Yang, Ming, Liu, Jiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	autoencoder Computed tomography Datasets Decoders Diagnosis Gompertz curves Gompertz function Growth kinetics growth prediction Growth rate Humans Lung Lung cancer Lung Neoplasms - diagnostic imaging Lung Neoplasms - pathology Lung nodules Mathematical models Medical imaging Morphology Nodules Parameterization Predictive models Progressions Pulmonary nodule Radiographic Image Interpretation, Computer-Assisted - methods Solitary Pulmonary Nodule - diagnostic imaging Task analysis Tomography, X-Ray Computed - methods Tumors Visualization
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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