BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

129 related articles for article (PubMed ID: 38688129)

  • 1. Nodule-CLIP: Lung nodule classification based on multi-modal contrastive learning.
    Sun L; Zhang M; Lu Y; Zhu W; Yi Y; Yan F
    Comput Biol Med; 2024 Jun; 175():108505. PubMed ID: 38688129
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning.
    Zhang S; Sun F; Wang N; Zhang C; Yu Q; Zhang M; Babyn P; Zhong H
    J Digit Imaging; 2019 Dec; 32(6):995-1007. PubMed ID: 31044393
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.
    Wang Z; Xin J; Sun P; Lin Z; Yao Y; Gao X
    Comput Methods Programs Biomed; 2018 Aug; 162():197-209. PubMed ID: 29903487
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss.
    Tran GS; Nghiem TP; Nguyen VT; Luong CM; Burie JC
    J Healthc Eng; 2019; 2019():5156416. PubMed ID: 30863524
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification.
    Dai Y; Yan S; Zheng B; Song C
    Phys Med Biol; 2018 Dec; 63(24):245004. PubMed ID: 30524071
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Robust explanation supervision for false positive reduction in pulmonary nodule detection.
    Zhao Q; Chang CW; Yang X; Zhao L
    Med Phys; 2024 Mar; 51(3):1687-1701. PubMed ID: 38224306
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT.
    Xie Y; Xia Y; Zhang J; Song Y; Feng D; Fulham M; Cai W
    IEEE Trans Med Imaging; 2019 Apr; 38(4):991-1004. PubMed ID: 30334786
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules.
    Zhao J; Zhang C; Li D; Niu J
    J Digit Imaging; 2020 Aug; 33(4):869-878. PubMed ID: 32285220
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies.
    Nasrullah N; Sang J; Alam MS; Mateen M; Cai B; Hu H
    Sensors (Basel); 2019 Aug; 19(17):. PubMed ID: 31466261
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Quantitative CT analysis of pulmonary nodules for lung adenocarcinoma risk classification based on an exponential weighted grey scale angular density distribution feature.
    Le V; Yang D; Zhu Y; Zheng B; Bai C; Shi H; Hu J; Zhai C; Lu S
    Comput Methods Programs Biomed; 2018 Jul; 160():141-151. PubMed ID: 29728241
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 3D gray density coding feature for benign-malignant pulmonary nodule classification on chest CT.
    Zheng B; Yang D; Zhu Y; Liu Y; Hu J; Bai C
    Med Phys; 2021 Dec; 48(12):7826-7836. PubMed ID: 34655238
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 3D multi-view squeeze-and-excitation convolutional neural network for lung nodule classification.
    Yang Y; Li X; Fu J; Han Z; Gao B
    Med Phys; 2023 Mar; 50(3):1905-1916. PubMed ID: 36639958
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database.
    Jacobs C; van Rikxoort EM; Murphy K; Prokop M; Schaefer-Prokop CM; van Ginneken B
    Eur Radiol; 2016 Jul; 26(7):2139-47. PubMed ID: 26443601
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.
    Xie Y; Zhang J; Xia Y
    Med Image Anal; 2019 Oct; 57():237-248. PubMed ID: 31352126
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks.
    Onishi Y; Teramoto A; Tsujimoto M; Tsukamoto T; Saito K; Toyama H; Imaizumi K; Fujita H
    Biomed Res Int; 2019; 2019():6051939. PubMed ID: 30719445
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification.
    Liu H; Cao H; Song E; Ma G; Xu X; Jin R; Liu C; Hung CC
    J Digit Imaging; 2020 Oct; 33(5):1242-1256. PubMed ID: 32607905
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Self-Supervised Transfer Learning Based on Domain Adaptation for Benign-Malignant Lung Nodule Classification on Thoracic CT.
    Huang H; Wu R; Li Y; Peng C
    IEEE J Biomed Health Inform; 2022 Aug; 26(8):3860-3871. PubMed ID: 35503850
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative.
    Wang W; Luo J; Yang X; Lin H
    Acad Radiol; 2015 Apr; 22(4):488-95. PubMed ID: 25601306
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method.
    Jung H; Kim B; Lee I; Lee J; Kang J
    BMC Med Imaging; 2018 Dec; 18(1):48. PubMed ID: 30509191
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.