192 related articles for article (PubMed ID: 37374292)
1. Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images.
Zhang Y; Feng W; Wu Z; Li W; Tao L; Liu X; Zhang F; Gao Y; Huang J; Guo X
Medicina (Kaunas); 2023 Jun; 59(6):. PubMed ID: 37374292
[No Abstract] [Full Text] [Related]
2. A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules.
Lin RY; Zheng YN; Lv FJ; Fu BJ; Li WJ; Liang ZR; Chu ZG
Med Phys; 2023 May; 50(5):2835-2843. PubMed ID: 36810703
[TBL] [Abstract][Full Text] [Related]
3. Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis.
Wang Q; Xu S; Zhang G; Zhang X; Gu J; Yang S; Zeng M; Zhang Z
J Appl Clin Med Phys; 2022 Nov; 23(11):e13759. PubMed ID: 35998185
[TBL] [Abstract][Full Text] [Related]
4. Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images.
Wang J; Chen X; Lu H; Zhang L; Pan J; Bao Y; Su J; Qian D
Med Phys; 2020 Apr; 47(4):1738-1749. PubMed ID: 32020649
[TBL] [Abstract][Full Text] [Related]
5. Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.
Zhang X; Dong X; Saripan MIB; Du D; Wu Y; Wang Z; Cao Z; Wen D; Liu Y; Marhaban MH
Thorac Cancer; 2023 Jul; 14(19):1802-1811. PubMed ID: 37183577
[TBL] [Abstract][Full Text] [Related]
6. CLSSL-ResNet: Predicting malignancy of solitary pulmonary nodules from CT images by chimeric label with self-supervised learning.
Zhao T; Qi S; Yue Y; Zhang B; Li J; Wen Y; Yao Y; Qian W; Guan Y
J Xray Sci Technol; 2023; 31(5):981-999. PubMed ID: 37424490
[TBL] [Abstract][Full Text] [Related]
7. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.
Liu A; Wang Z; Yang Y; Wang J; Dai X; Wang L; Lu Y; Xue F
Cancer Commun (Lond); 2020 Jan; 40(1):16-24. PubMed ID: 32125097
[TBL] [Abstract][Full Text] [Related]
8. Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT.
Lin CY; Guo SM; Lien JJ; Lin WT; Liu YS; Lai CH; Hsu IL; Chang CC; Tseng YL
Radiol Med; 2024 Jan; 129(1):56-69. PubMed ID: 37971691
[TBL] [Abstract][Full Text] [Related]
9. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.
Hu X; Gong J; Zhou W; Li H; Wang S; Wei M; Peng W; Gu Y
Phys Med Biol; 2021 Mar; 66(6):065015. PubMed ID: 33596552
[TBL] [Abstract][Full Text] [Related]
10. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.
Tu SJ; Wang CW; Pan KT; Wu YC; Wu CT
Phys Med Biol; 2018 Mar; 63(6):065005. PubMed ID: 29446758
[TBL] [Abstract][Full Text] [Related]
11. Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters.
Liang L; Zhang H; Lei H; Zhou H; Wu Y; Shen J
Technol Cancer Res Treat; 2022; 21():15330338221119748. PubMed ID: 36259167
[No Abstract] [Full Text] [Related]
12. The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.
Zhang R; Wei Y; Shi F; Ren J; Zhou Q; Li W; Chen B
BMC Cancer; 2022 Nov; 22(1):1118. PubMed ID: 36319968
[TBL] [Abstract][Full Text] [Related]
13. Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning.
Zhao X; Qi S; Zhang B; Ma H; Qian W; Yao Y; Sun J
J Xray Sci Technol; 2019; 27(4):615-629. PubMed ID: 31227682
[TBL] [Abstract][Full Text] [Related]
14. One deep learning local-global model based on CT imaging to differentiate between nodular cryptococcosis and lung cancer which are hard to be diagnosed.
Li S; Zhang G; Yin Y; Xie Q; Guo X; Cao K; Song Q; Guan J; Zhou X
Comput Med Imaging Graph; 2021 Dec; 94():102009. PubMed ID: 34741847
[TBL] [Abstract][Full Text] [Related]
15. Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules.
Gong J; Liu JY; Sun XW; Zheng B; Nie SD
Phys Med Biol; 2018 Feb; 63(3):035036. PubMed ID: 29311420
[TBL] [Abstract][Full Text] [Related]
16. Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study.
Chae KJ; Jin GY; Ko SB; Wang Y; Zhang H; Choi EJ; Choi H
Acad Radiol; 2020 Apr; 27(4):e55-e63. PubMed ID: 31780395
[TBL] [Abstract][Full Text] [Related]
17. Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images.
Shi F; Chen B; Cao Q; Wei Y; Zhou Q; Zhang R; Zhou Y; Yang W; Wang X; Fan R; Yang F; Chen Y; Li W; Gao Y; Shen D
IEEE Trans Med Imaging; 2022 Apr; 41(4):771-781. PubMed ID: 34705640
[TBL] [Abstract][Full Text] [Related]
18. Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.
Zhang C; Sun X; Dang K; Li K; Guo XW; Chang J; Yu ZQ; Huang FY; Wu YS; Liang Z; Liu ZY; Zhang XG; Gao XL; Huang SH; Qin J; Feng WN; Zhou T; Zhang YB; Fang WJ; Zhao MF; Yang XN; Zhou Q; Wu YL; Zhong WZ
Oncologist; 2019 Sep; 24(9):1159-1165. PubMed ID: 30996009
[TBL] [Abstract][Full Text] [Related]
19. A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.
Zhao HB; Liu C; Ye J; Chang LF; Xu Q; Shi BW; Liu LL; Yin YL; Shi BB
Endokrynol Pol; 2021; 72(3):217-225. PubMed ID: 33619712
[TBL] [Abstract][Full Text] [Related]
20. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.
Choi W; Oh JH; Riyahi S; Liu CJ; Jiang F; Chen W; White C; Rimner A; Mechalakos JG; Deasy JO; Lu W
Med Phys; 2018 Apr; 45(4):1537-1549. PubMed ID: 29457229
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
