BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

373 related articles for article (PubMed ID: 33631891)

  • 1. [A deep learning-based lung nodule density classification and segmentation method and its effectiveness under different CT reconstruction algorithms].
    Meng XL; Xing ZJ; Lu S
    Zhonghua Yi Xue Za Zhi; 2021 Feb; 101(7):476-480. PubMed ID: 33631891
    [No Abstract]   [Full Text] [Related]  

  • 2. Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction.
    Lu Z; Long F; He X
    Comput Math Methods Med; 2022; 2022():3490463. PubMed ID: 35495882
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images.
    Wang Y; Zhou C; Chan HP; Hadjiiski LM; Chughtai A; Kazerooni EA
    Med Phys; 2022 Nov; 49(11):7287-7302. PubMed ID: 35717560
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Design of lung nodules segmentation and recognition algorithm based on deep learning.
    Yu H; Li J; Zhang L; Cao Y; Yu X; Sun J
    BMC Bioinformatics; 2021 Nov; 22(Suppl 5):314. PubMed ID: 34749636
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation.
    Dong X; Xu S; Liu Y; Wang A; Saripan MI; Li L; Zhang X; Lu L
    Cancer Imaging; 2020 Aug; 20(1):53. PubMed ID: 32738913
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination.
    Zhang C; Li J; Huang J; Wu S
    J Healthc Eng; 2021; 2021():3417285. PubMed ID: 34721823
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models.
    Cascio D; Magro R; Fauci F; Iacomi M; Raso G
    Comput Biol Med; 2012 Nov; 42(11):1098-109. PubMed ID: 23020972
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation.
    Singadkar G; Mahajan A; Thakur M; Talbar S
    J Digit Imaging; 2020 Jun; 33(3):678-684. PubMed ID: 32026218
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
    Alilou M; Beig N; Orooji M; Rajiah P; Velcheti V; Rakshit S; Reddy N; Yang M; Jacono F; Gilkeson RC; Linden P; Madabhushi A
    Med Phys; 2017 Jul; 44(7):3556-3569. PubMed ID: 28295386
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification.
    Ren Y; Tsai MY; Chen L; Wang J; Li S; Liu Y; Jia X; Shen C
    Int J Comput Assist Radiol Surg; 2020 Feb; 15(2):287-295. PubMed ID: 31768885
    [TBL] [Abstract][Full Text] [Related]  

  • 14. CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation.
    Pezzano G; Ribas Ripoll V; Radeva P
    Comput Methods Programs Biomed; 2021 Jan; 198():105792. PubMed ID: 33130496
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.
    Ohno Y; Aoyagi K; Yaguchi A; Seki S; Ueno Y; Kishida Y; Takenaka D; Yoshikawa T
    Radiology; 2020 Aug; 296(2):432-443. PubMed ID: 32452736
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Discrimination between transient and persistent subsolid pulmonary nodules on baseline CT using deep transfer learning.
    Huang C; Lv W; Zhou C; Mao L; Xu Q; Li X; Qi L; Xia F; Li X; Zhang Q; Zhang L; Lu G
    Eur Radiol; 2020 Dec; 30(12):6913-6923. PubMed ID: 32696253
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Three-stage segmentation of lung region from CT images using deep neural networks.
    Osadebey M; Andersen HK; Waaler D; Fossaa K; Martinsen ACT; Pedersen M
    BMC Med Imaging; 2021 Jul; 21(1):112. PubMed ID: 34266391
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs.
    Yoo H; Kim KH; Singh R; Digumarthy SR; Kalra MK
    JAMA Netw Open; 2020 Sep; 3(9):e2017135. PubMed ID: 32970157
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Lung nodule segmentation using Salp Shuffled Shepherd Optimization Algorithm-based Generative Adversarial Network.
    Jain S; Indora S; Atal DK
    Comput Biol Med; 2021 Oct; 137():104811. PubMed ID: 34492518
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 19.