134 related articles for article (PubMed ID: 36093645)
1. A deep learning-based method for cervical transformation zone classification in colposcopy images.
Cao Y; Ma H; Fan Y; Liu Y; Zhang H; Cao C; Yu H
Technol Health Care; 2023; 31(2):527-538. PubMed ID: 36093645
[TBL] [Abstract][Full Text] [Related]
2. MSCI: A multistate dataset for colposcopy image classification of cervical cancer screening.
Yu Y; Ma J; Zhao W; Li Z; Ding S
Int J Med Inform; 2021 Feb; 146():104352. PubMed ID: 33360117
[TBL] [Abstract][Full Text] [Related]
3. Deep learning based cervical screening by the cross-modal integration of colposcopy, cytology, and HPV test.
Fu L; Xia W; Shi W; Cao GX; Ruan YT; Zhao XY; Liu M; Niu SM; Li F; Gao X
Int J Med Inform; 2022 Mar; 159():104675. PubMed ID: 34979436
[TBL] [Abstract][Full Text] [Related]
4. Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images.
Chandran V; Sumithra MG; Karthick A; George T; Deivakani M; Elakkiya B; Subramaniam U; Manoharan S
Biomed Res Int; 2021; 2021():5584004. PubMed ID: 33997017
[TBL] [Abstract][Full Text] [Related]
5. Diagnosis of cervical precancerous lesions based on multimodal feature changes.
Peng G; Dong H; Liang T; Li L; Liu J
Comput Biol Med; 2021 Mar; 130():104209. PubMed ID: 33440316
[TBL] [Abstract][Full Text] [Related]
6. Colposcopic multimodal fusion for the classification of cervical lesions.
Fan Y; Ma H; Fu Y; Liang X; Yu H; Liu Y
Phys Med Biol; 2022 Jun; 67(13):. PubMed ID: 35617940
[No Abstract] [Full Text] [Related]
7. Recognition of Cervical Precancerous Lesions Based on Probability Distribution Feature Guidance.
Liu J; Sun X; Li R; Peng Y
Curr Med Imaging; 2022; 18(11):1204-1213. PubMed ID: 36062868
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Comparison of accuracy and reproducibility of colposcopic impression based on a single image versus a two-minute time series of colposcopic images.
Perkins R; Jeronimo J; Hammer A; Novetsky A; Guido R; Del Pino M; Louwers J; Marcus J; Resende C; Smith K; Egemen D; Befano B; Smith D; Antani S; de Sanjose S; Schiffman M
Gynecol Oncol; 2022 Oct; 167(1):89-95. PubMed ID: 36008184
[TBL] [Abstract][Full Text] [Related]
10. Computer-Aided Cervical Cancer Diagnosis Using Time-Lapsed Colposcopic Images.
Li Y; Chen J; Xue P; Tang C; Chang J; Chu C; Ma K; Li Q; Zheng Y; Qiao Y
IEEE Trans Med Imaging; 2020 Nov; 39(11):3403-3415. PubMed ID: 32406830
[TBL] [Abstract][Full Text] [Related]
11. The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence.
Xue P; Ng MTA; Qiao Y
BMC Med; 2020 Jun; 18(1):169. PubMed ID: 32493320
[TBL] [Abstract][Full Text] [Related]
12. Automatic segmentation of cervical region in colposcopic images using K-means.
Bai B; Liu PZ; Du YZ; Luo YM
Australas Phys Eng Sci Med; 2018 Dec; 41(4):1077-1085. PubMed ID: 30215221
[TBL] [Abstract][Full Text] [Related]
13. An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.
Hu L; Bell D; Antani S; Xue Z; Yu K; Horning MP; Gachuhi N; Wilson B; Jaiswal MS; Befano B; Long LR; Herrero R; Einstein MH; Burk RD; Demarco M; Gage JC; Rodriguez AC; Wentzensen N; Schiffman M
J Natl Cancer Inst; 2019 Sep; 111(9):923-932. PubMed ID: 30629194
[TBL] [Abstract][Full Text] [Related]
14. Improving colposcopic accuracy for cervical precancer detection: a retrospective multicenter study in China.
Wei B; Zhang B; Xue P; Seery S; Wang J; Li Q; Jiang Y; Qiao Y
BMC Cancer; 2022 Apr; 22(1):388. PubMed ID: 35399061
[TBL] [Abstract][Full Text] [Related]
15. Cervical Cancer Diagnostics Healthcare System Using Hybrid Object Detection Adversarial Networks.
Elakkiya R; Subramaniyaswamy V; Vijayakumar V; Mahanti A
IEEE J Biomed Health Inform; 2022 Apr; 26(4):1464-1471. PubMed ID: 34214045
[TBL] [Abstract][Full Text] [Related]
16. Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer.
Brenes D; Barberan CJ; Hunt B; Parra SG; Salcedo MP; Possati-Resende JC; Cremer ML; Castle PE; Fregnani JHTG; Maza M; Schmeler KM; Baraniuk R; Richards-Kortum R
Comput Med Imaging Graph; 2022 Apr; 97():102052. PubMed ID: 35299096
[TBL] [Abstract][Full Text] [Related]
17. DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques.
Rahaman MM; Li C; Yao Y; Kulwa F; Wu X; Li X; Wang Q
Comput Biol Med; 2021 Sep; 136():104649. PubMed ID: 34332347
[TBL] [Abstract][Full Text] [Related]
18. An Improved Image Classification Method for Cervical Precancerous Lesions Based on ShuffleNet.
Fang S; Yang J; Wang M; Liu C; Liu S
Comput Intell Neurosci; 2022; 2022():9675628. PubMed ID: 36148422
[TBL] [Abstract][Full Text] [Related]
19. Cervical Transformation Zone Segmentation and Classification based on Improved Inception-ResNet-V2 Using Colposcopy Images.
Dash S; Sethy PK; Behera SK
Cancer Inform; 2023; 22():11769351231161477. PubMed ID: 37008072
[TBL] [Abstract][Full Text] [Related]
20. Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.
Xue P; Tang C; Li Q; Li Y; Shen Y; Zhao Y; Chen J; Wu J; Li L; Wang W; Li Y; Cui X; Zhang S; Zhang W; Zhang X; Ma K; Zheng Y; Qian T; Ng MTA; Liu Z; Qiao Y; Jiang Y; Zhao F
BMC Med; 2020 Dec; 18(1):406. PubMed ID: 33349257
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]