2021
DOI: 10.1002/ima.22642
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3D multi‐resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules

Abstract: To accurately diagnose multiple pathological types of pulmonary nodules based on lung computed tomography (CT) images, a multi‐resolution three‐dimensional (3D) multi‐classification deep learning model (Mr‐Mc) was proposed. The Mr‐Mc model was constructed by using our own constructed lung CT image dataset of pulmonary nodules with clinical pathological information (LCID‐CPI), which can accurately diagnose inflammation, squamous cell carcinoma, adenocarcinoma, and other benign diseases. In order to process nodu… Show more

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Cited by 4 publications
(2 citation statements)
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“…Precisely detecting landmarks in medical images has emerged as a critical field 2 . Existing computer‐aided diagnosis methods play a significant role in numerous applications, including medical image registration, 3 tissue segmentation, 4 parameter measurement, 5 pathological diagnosis, 6 treatment planning, 7 surgical guidance, 8 and initialization processes within the domain of other medical image processing 9 . However, due to the diversity of human anatomical structures, some anatomical entities include closely located or potentially locally similar landmarks.…”
Section: Introductionmentioning
confidence: 99%
“…Precisely detecting landmarks in medical images has emerged as a critical field 2 . Existing computer‐aided diagnosis methods play a significant role in numerous applications, including medical image registration, 3 tissue segmentation, 4 parameter measurement, 5 pathological diagnosis, 6 treatment planning, 7 surgical guidance, 8 and initialization processes within the domain of other medical image processing 9 . However, due to the diversity of human anatomical structures, some anatomical entities include closely located or potentially locally similar landmarks.…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning technologies, especially convolutional neural networks (CNNs), have become powerful tools for improving lung nodule detection and classification performance due to their ability to automatically learn complex and abstract features from large datasets. However, the performance of deep learning models largely depends on the availability of large amounts of annotated data, which is often difficult to obtain in medical imaging analysis [10][11][12].…”
Section: Introductionmentioning
confidence: 99%