175 related articles for article (PubMed ID: 30763802)
1. Deep learning for cell image segmentation and ranking.
Araújo FHD; Silva RRV; Ushizima DM; Rezende MT; Carneiro CM; Campos Bianchi AG; Medeiros FNS
Comput Med Imaging Graph; 2019 Mar; 72():13-21. PubMed ID: 30763802
[TBL] [Abstract][Full Text] [Related]
2. Deep Convolution Neural Network for Malignancy Detection and Classification in Microscopic Uterine Cervix Cell Images.
P B S; Faruqi F; K S H; Kudva R
Asian Pac J Cancer Prev; 2019 Nov; 20(11):3447-3456. PubMed ID: 31759371
[TBL] [Abstract][Full Text] [Related]
3. Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image.
Sornapudi S; Brown GT; Xue Z; Long R; Allen L; Antani S
AMIA Annu Symp Proc; 2019; 2019():820-827. PubMed ID: 32308878
[TBL] [Abstract][Full Text] [Related]
4. A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network.
Hussain E; Mahanta LB; Das CR; Talukdar RK
Tissue Cell; 2020 Aug; 65():101347. PubMed ID: 32746984
[TBL] [Abstract][Full Text] [Related]
5. A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images.
Hussain E; Mahanta LB; Das CR; Choudhury M; Chowdhury M
Artif Intell Med; 2020 Jul; 107():101897. PubMed ID: 32828445
[TBL] [Abstract][Full Text] [Related]
6. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images.
William W; Ware A; Basaza-Ejiri AH; Obungoloch J
Comput Methods Programs Biomed; 2018 Oct; 164():15-22. PubMed ID: 30195423
[TBL] [Abstract][Full Text] [Related]
7. Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images.
Song Y; Tan EL; Jiang X; Cheng JZ; Ni D; Chen S; Lei B; Wang T
IEEE Trans Med Imaging; 2017 Jan; 36(1):288-300. PubMed ID: 27623573
[TBL] [Abstract][Full Text] [Related]
8. A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images.
Cui Y; Zhang G; Liu Z; Xiong Z; Hu J
Med Biol Eng Comput; 2019 Sep; 57(9):2027-2043. PubMed ID: 31346949
[TBL] [Abstract][Full Text] [Related]
9. Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.
Ma X; Wei J; Zhou C; Helvie MA; Chan HP; Hadjiiski LM; Lu Y
Med Phys; 2019 May; 46(5):2103-2114. PubMed ID: 30771257
[TBL] [Abstract][Full Text] [Related]
10. U-Net based deep learning bladder segmentation in CT urography.
Ma X; Hadjiiski LM; Wei J; Chan HP; Cha KH; Cohan RH; Caoili EM; Samala R; Zhou C; Lu Y
Med Phys; 2019 Apr; 46(4):1752-1765. PubMed ID: 30734932
[TBL] [Abstract][Full Text] [Related]
11. An efficient Fusion-Purification Network for Cervical pap-smear image classification.
Yang T; Hu H; Li X; Meng Q; Lu H; Huang Q
Comput Methods Programs Biomed; 2024 Jun; 251():108199. PubMed ID: 38728830
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells.
Lu Z; Carneiro G; Bradley AP; Ushizima D; Nosrati MS; Bianchi AGC; Carneiro CM; Hamarneh G
IEEE J Biomed Health Inform; 2017 Mar; 21(2):441-450. PubMed ID: 26800556
[TBL] [Abstract][Full Text] [Related]
13. Nucleus segmentation and classification using residual SE-UNet and feature concatenation approach incervical cytopathology cell images.
Chowdary GJ; G S; M P; Yogarajah P
Technol Cancer Res Treat; 2023; 22():15330338221134833. PubMed ID: 36744768
[No Abstract] [Full Text] [Related]
14. Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture.
Attia M; Hossny M; Zhou H; Nahavandi S; Asadi H; Yazdabadi A
Comput Methods Programs Biomed; 2019 Aug; 177():17-30. PubMed ID: 31319945
[TBL] [Abstract][Full Text] [Related]
15. A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images.
William W; Ware A; Basaza-Ejiri AH; Obungoloch J
Biomed Eng Online; 2019 Feb; 18(1):16. PubMed ID: 30755214
[TBL] [Abstract][Full Text] [Related]
16. Automatic segmentation of hyperreflective foci in OCT images.
Varga L; Kovács A; Grósz T; Thury G; Hadarits F; Dégi R; Dombi J
Comput Methods Programs Biomed; 2019 Sep; 178():91-103. PubMed ID: 31416566
[TBL] [Abstract][Full Text] [Related]
17. Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior.
Chen L; Shen C; Zhou Z; Maquilan G; Albuquerque K; Folkert MR; Wang J
Phys Med Biol; 2019 Apr; 64(8):085019. PubMed ID: 30818303
[TBL] [Abstract][Full Text] [Related]
18. Automated segmentation of free-lying cell nuclei in Pap smears for malignancy-associated change analysis.
Moshavegh R; Ehteshami Bejnordi B; Mehnert A; Sujathan K; Malm P; Bengtsson E
Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():5372-5. PubMed ID: 23367143
[TBL] [Abstract][Full Text] [Related]
19. Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models.
Tan SL; Selvachandran G; Ding W; Paramesran R; Kotecha K
Interdiscip Sci; 2024 Mar; 16(1):16-38. PubMed ID: 37962777
[TBL] [Abstract][Full Text] [Related]
20. An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells.
Zhi Lu ; Carneiro G; Bradley AP
IEEE Trans Image Process; 2015 Apr; 24(4):1261-72. PubMed ID: 25585419
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
