253 related articles for article (PubMed ID: 31905154)
1. A Two-Stage Convolutional Neural Networks for Lung Nodule Detection.
Cao H; Liu H; Song E; Ma G; Xu X; Jin R; Liu T; Hung CC
IEEE J Biomed Health Inform; 2020 Jul; 24(7):2006-2015. PubMed ID: 31905154
[TBL] [Abstract][Full Text] [Related]
2. A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning.
Huang W; Xue Y; Wu Y
PLoS One; 2019; 14(7):e0219369. PubMed ID: 31299053
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection.
Zheng S; Guo J; Cui X; Veldhuis RNJ; Oudkerk M; van Ooijen PMA
IEEE Trans Med Imaging; 2020 Mar; 39(3):797-805. PubMed ID: 31425026
[TBL] [Abstract][Full Text] [Related]
5. Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection.
Eun H; Kim D; Jung C; Kim C
Comput Methods Programs Biomed; 2018 Oct; 165():215-224. PubMed ID: 30337076
[TBL] [Abstract][Full Text] [Related]
6. Pulmonary nodule detection in CT scans with equivariant CNNs.
Winkels M; Cohen TS
Med Image Anal; 2019 Jul; 55():15-26. PubMed ID: 31003034
[TBL] [Abstract][Full Text] [Related]
7. Detection of pulmonary nodules based on a multiscale feature 3D U-Net convolutional neural network of transfer learning.
Tang S; Yang M; Bai J
PLoS One; 2020; 15(8):e0235672. PubMed ID: 32845877
[TBL] [Abstract][Full Text] [Related]
8. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs.
Gu Y; Lu X; Yang L; Zhang B; Yu D; Zhao Y; Gao L; Wu L; Zhou T
Comput Biol Med; 2018 Dec; 103():220-231. PubMed ID: 30390571
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Two-stage lung nodule detection framework using enhanced UNet and convolutional LSTM networks in CT images.
Akila Agnes S; Anitha J; Arun Solomon A
Comput Biol Med; 2022 Oct; 149():106059. PubMed ID: 36087510
[TBL] [Abstract][Full Text] [Related]
11. Pulmonary nodule detection using hybrid two-stage 3D CNNs.
Tan M; Wu F; Yang B; Ma J; Kong D; Chen Z; Long D
Med Phys; 2020 Aug; 47(8):3376-3388. PubMed ID: 32239521
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. An efficient multi-path 3D convolutional neural network for false-positive reduction of pulmonary nodule detection.
Yuan H; Fan Z; Wu Y; Cheng J
Int J Comput Assist Radiol Surg; 2021 Dec; 16(12):2269-2277. PubMed ID: 34449037
[TBL] [Abstract][Full Text] [Related]
14. 3-D Convolutional Neural Networks for Automatic Detection of Pulmonary Nodules in Chest CT.
Pezeshk A; Hamidian S; Petrick N; Sahiner B
IEEE J Biomed Health Inform; 2019 Sep; 23(5):2080-2090. PubMed ID: 30418929
[TBL] [Abstract][Full Text] [Related]
15. Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier.
Ahmadyar Y; Kamali-Asl A; Arabi H; Samimi R; Zaidi H
Radiol Phys Technol; 2024 Mar; 17(1):124-134. PubMed ID: 37980315
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction.
Omigbodun AO; Noo F; McNitt-Gray M; Hsu W; Hsieh SS
Med Phys; 2019 Oct; 46(10):4563-4574. PubMed ID: 31396974
[TBL] [Abstract][Full Text] [Related]
18. Dual-branch feature fusion S3D V-Net network for lung nodules segmentation.
Xu X; Du L; Yin D
J Appl Clin Med Phys; 2024 Jun; 25(6):e14331. PubMed ID: 38478388
[TBL] [Abstract][Full Text] [Related]
19. Automatic pulmonary ground-glass opacity nodules detection and classification based on 3D neural network.
Ma H; Guo H; Zhao M; Qi S; Li H; Tian Y; Li Z; Zhang G; Yao Y; Qian W
Med Phys; 2022 Apr; 49(4):2555-2569. PubMed ID: 35092608
[TBL] [Abstract][Full Text] [Related]
20. Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.
Teramoto A; Fujita H; Yamamuro O; Tamaki T
Med Phys; 2016 Jun; 43(6):2821-2827. PubMed ID: 27277030
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
