298 related articles for article (PubMed ID: 31767541)
1. ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.
Wang S; Wang T; Yang L; Yang DM; Fujimoto J; Yi F; Luo X; Yang Y; Yao B; Lin S; Moran C; Kalhor N; Weissferdt A; Minna J; Xie Y; Wistuba II; Mao Y; Xiao G
EBioMedicine; 2019 Dec; 50():103-110. PubMed ID: 31767541
[TBL] [Abstract][Full Text] [Related]
2. White blood cells detection and classification based on regional convolutional neural networks.
Kutlu H; Avci E; Özyurt F
Med Hypotheses; 2020 Feb; 135():109472. PubMed ID: 31760248
[TBL] [Abstract][Full Text] [Related]
3. Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome.
Wang S; Chen A; Yang L; Cai L; Xie Y; Fujimoto J; Gazdar A; Xiao G
Sci Rep; 2018 Jul; 8(1):10393. PubMed ID: 29991684
[TBL] [Abstract][Full Text] [Related]
4. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.
Gong J; Liu J; Hao W; Nie S; Zheng B; Wang S; Peng W
Eur Radiol; 2020 Apr; 30(4):1847-1855. PubMed ID: 31811427
[TBL] [Abstract][Full Text] [Related]
5. Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides.
Gertych A; Swiderska-Chadaj Z; Ma Z; Ing N; Markiewicz T; Cierniak S; Salemi H; Guzman S; Walts AE; Knudsen BS
Sci Rep; 2019 Feb; 9(1):1483. PubMed ID: 30728398
[TBL] [Abstract][Full Text] [Related]
6. A dual data stream hybrid neural network for classifying pathological images of lung adenocarcinoma.
Li L; Mei Z; Li Y; Yu Y; Liu M
Comput Biol Med; 2024 Jun; 175():108519. PubMed ID: 38688128
[TBL] [Abstract][Full Text] [Related]
7. Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks.
Antonio VAA; Ono N; Saito A; Sato T; Altaf-Ul-Amin M; Kanaya S
Int J Comput Assist Radiol Surg; 2018 Dec; 13(12):1905-1913. PubMed ID: 30159833
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images.
Khosravi P; Kazemi E; Imielinski M; Elemento O; Hajirasouliha I
EBioMedicine; 2018 Jan; 27():317-328. PubMed ID: 29292031
[TBL] [Abstract][Full Text] [Related]
10. Development and Validation of a Pathology Image Analysis-based Predictive Model for Lung Adenocarcinoma Prognosis - A Multi-cohort Study.
Luo X; Yin S; Yang L; Fujimoto J; Yang Y; Moran C; Kalhor N; Weissferdt A; Xie Y; Gazdar A; Minna J; Wistuba II; Mao Y; Xiao G
Sci Rep; 2019 May; 9(1):6886. PubMed ID: 31053738
[TBL] [Abstract][Full Text] [Related]
11. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.
Wei JW; Tafe LJ; Linnik YA; Vaickus LJ; Tomita N; Hassanpour S
Sci Rep; 2019 Mar; 9(1):3358. PubMed ID: 30833650
[TBL] [Abstract][Full Text] [Related]
12. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.
Feng B; Chen X; Chen Y; Lu S; Liu K; Li K; Liu Z; Hao Y; Li Z; Zhu Z; Yao N; Liang G; Zhang J; Long W; Liu X
Eur Radiol; 2020 Dec; 30(12):6497-6507. PubMed ID: 32594210
[TBL] [Abstract][Full Text] [Related]
13. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features.
Xu Y; Jia Z; Wang LB; Ai Y; Zhang F; Lai M; Chang EI
BMC Bioinformatics; 2017 May; 18(1):281. PubMed ID: 28549410
[TBL] [Abstract][Full Text] [Related]
14. Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer.
Rączkowska A; Paśnik I; Kukiełka M; Nicoś M; Budzinska MA; Kucharczyk T; Szumiło J; Krawczyk P; Crosetto N; Szczurek E
BMC Cancer; 2022 Sep; 22(1):1001. PubMed ID: 36131239
[TBL] [Abstract][Full Text] [Related]
15. Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma.
Huang H; Zheng D; Chen H; Wang Y; Chen C; Xu L; Li G; Wang Y; He X; Li W
Med Phys; 2022 Oct; 49(10):6384-6394. PubMed ID: 35938604
[TBL] [Abstract][Full Text] [Related]
16. Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis.
Luo X; Zang X; Yang L; Huang J; Liang F; Rodriguez-Canales J; Wistuba II; Gazdar A; Xie Y; Xiao G
J Thorac Oncol; 2017 Mar; 12(3):501-509. PubMed ID: 27826035
[TBL] [Abstract][Full Text] [Related]
17. Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.
Yang JW; Song DH; An HJ; Seo SB
Sci Rep; 2022 Feb; 12(1):1830. PubMed ID: 35115593
[TBL] [Abstract][Full Text] [Related]
18.
Sibille L; Seifert R; Avramovic N; Vehren T; Spottiswoode B; Zuehlsdorff S; Schäfers M
Radiology; 2020 Feb; 294(2):445-452. PubMed ID: 31821122
[TBL] [Abstract][Full Text] [Related]
19. Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.
Yu KH; Wang F; Berry GJ; Ré C; Altman RB; Snyder M; Kohane IS
J Am Med Inform Assoc; 2020 May; 27(5):757-769. PubMed ID: 32364237
[TBL] [Abstract][Full Text] [Related]
20. Integrative analysis of imaging and transcriptomic data of the immune landscape associated with tumor metabolism in lung adenocarcinoma: Clinical and prognostic implications.
Choi H; Na KJ
Theranostics; 2018; 8(7):1956-1965. PubMed ID: 29556367
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
