649 related articles for article (PubMed ID: 30649670)
1. A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation.
Laves MH; Bicker J; Kahrs LA; Ortmaier T
Int J Comput Assist Radiol Surg; 2019 Mar; 14(3):483-492. PubMed ID: 30649670
[TBL] [Abstract][Full Text] [Related]
2. A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound.
Gómez-Flores W; Coelho de Albuquerque Pereira W
Comput Biol Med; 2020 Nov; 126():104036. PubMed ID: 33059238
[TBL] [Abstract][Full Text] [Related]
3. The effects of different levels of realism on the training of CNNs with only synthetic images for the semantic segmentation of robotic instruments in a head phantom.
Heredia Perez SA; Marques Marinho M; Harada K; Mitsuishi M
Int J Comput Assist Radiol Surg; 2020 Aug; 15(8):1257-1265. PubMed ID: 32445129
[TBL] [Abstract][Full Text] [Related]
4. Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning.
Guo X; Zhang N; Guo J; Zhang H; Hao Y; Hang J
Med Phys; 2019 Dec; 46(12):5666-5676. PubMed ID: 31610020
[TBL] [Abstract][Full Text] [Related]
5. Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images.
Azam MA; Sampieri C; Ioppi A; Benzi P; Giordano GG; De Vecchi M; Campagnari V; Li S; Guastini L; Paderno A; Moccia S; Piazza C; Mattos LS; Peretti G
Front Oncol; 2022; 12():900451. PubMed ID: 35719939
[TBL] [Abstract][Full Text] [Related]
6. Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network.
Fehling MK; Grosch F; Schuster ME; Schick B; Lohscheller J
PLoS One; 2020; 15(2):e0227791. PubMed ID: 32040514
[TBL] [Abstract][Full Text] [Related]
7. Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images.
Hoar D; Lee PQ; Guida A; Patterson S; Bowen CV; Merrimen J; Wang C; Rendon R; Beyea SD; Clarke SE
Comput Methods Programs Biomed; 2021 Oct; 210():106375. PubMed ID: 34500139
[TBL] [Abstract][Full Text] [Related]
8. Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.
Parker F; Brodsky MB; Akst LM; Ali H
Ann Otol Rhinol Laryngol; 2021 Mar; 130(3):286-291. PubMed ID: 32795159
[TBL] [Abstract][Full Text] [Related]
9. A Convolutional Neural Network for Real Time Classification, Identification, and Labelling of Vocal Cord and Tracheal Using Laryngoscopy and Bronchoscopy Video.
Matava C; Pankiv E; Raisbeck S; Caldeira M; Alam F
J Med Syst; 2020 Jan; 44(2):44. PubMed ID: 31897740
[TBL] [Abstract][Full Text] [Related]
10. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
Tong N; Gou S; Yang S; Ruan D; Sheng K
Med Phys; 2018 Oct; 45(10):4558-4567. PubMed ID: 30136285
[TBL] [Abstract][Full Text] [Related]
11. Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs.
Lee JH; Han SS; Kim YH; Lee C; Kim I
Oral Surg Oral Med Oral Pathol Oral Radiol; 2020 Jun; 129(6):635-642. PubMed ID: 31992524
[TBL] [Abstract][Full Text] [Related]
12. Image generation by GAN and style transfer for agar plate image segmentation.
Andreini P; Bonechi S; Bianchini M; Mecocci A; Scarselli F
Comput Methods Programs Biomed; 2020 Feb; 184():105268. PubMed ID: 31891902
[TBL] [Abstract][Full Text] [Related]
13. Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset.
Xu M; Qi S; Yue Y; Teng Y; Xu L; Yao Y; Qian W
Biomed Eng Online; 2019 Jan; 18(1):2. PubMed ID: 30602393
[TBL] [Abstract][Full Text] [Related]
14. [Application of semantic segmentation based on convolutional neural network in medical images].
Wu Y; Lin L; Wang J; Wu S
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2020 Jun; 37(3):533-540. PubMed ID: 32597097
[TBL] [Abstract][Full Text] [Related]
15. Glomerulosclerosis identification in whole slide images using semantic segmentation.
Bueno G; Fernandez-Carrobles MM; Gonzalez-Lopez L; Deniz O
Comput Methods Programs Biomed; 2020 Feb; 184():105273. PubMed ID: 31891905
[TBL] [Abstract][Full Text] [Related]
16. Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing.
Guo J; Fu R; Pan L; Zheng S; Huang L; Zheng B; He B
Comput Methods Programs Biomed; 2022 Mar; 215():106610. PubMed ID: 35077902
[TBL] [Abstract][Full Text] [Related]
17. Ultrasound Image Segmentation: A Deeply Supervised Network With Attention to Boundaries.
Mishra D; Chaudhury S; Sarkar M; Soin AS
IEEE Trans Biomed Eng; 2019 Jun; 66(6):1637-1648. PubMed ID: 30346279
[TBL] [Abstract][Full Text] [Related]
18. Deep learning for colon cancer histopathological images analysis.
Ben Hamida A; Devanne M; Weber J; Truntzer C; Derangère V; Ghiringhelli F; Forestier G; Wemmert C
Comput Biol Med; 2021 Sep; 136():104730. PubMed ID: 34375901
[TBL] [Abstract][Full Text] [Related]
19. Semantic segmentation of mFISH images using convolutional networks.
Pardo E; Morgado JMT; Malpica N
Cytometry A; 2018 Jun; 93(6):620-627. PubMed ID: 29710381
[TBL] [Abstract][Full Text] [Related]
20. Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.
Al-Masni MA; Al-Antari MA; Choi MT; Han SM; Kim TS
Comput Methods Programs Biomed; 2018 Aug; 162():221-231. PubMed ID: 29903489
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
