|
1
|
Müller VC and Bostrom N: Future progress
in artificial intelligence: A survey of expert opinion. In:
Fundamental Issues of Artificial Intelligence. Springer; Cham: pp.
555–572. 2016
|
|
2
|
Silver D, Schrittwieser J, Simonyan K,
Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A,
et al: Mastering the game of go without human knowledge. Nature.
550:354–359. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Miyagi Y, Takehara K and Miyake T:
Application of deep learning to the classification of uterine
cervical squamous epithelial lesion from colposcopy images. Mol
Clin Oncol. 11:583–589. 2019.PubMed/NCBI
|
|
4
|
Miyagi Y, Habara T, Hirata R and Hayashi
N: Feasibility of predicting live birth by combining conventional
embryo evaluation with artificial intelligence applied to a
blastocyst image in patients classified by age. Reprod Med Biol.
18:344–356. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Arbyn M, Castellsagué X, de Sanjosé S,
Bruni L, Saraiya M, Bray F and Ferlay J: Worldwide burden of
cervical cancer in 2008. Ann Oncol. 22:2675–2686. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
García-Arteaga JD, Kybic J and Li W:
Automatic colposcopy video tissue classification using higher order
entropy-based image registration. Comput Biol Med. 41:960–970.
2011. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Kyrgiou M, Tsoumpou I, Vrekoussis T,
Martin-Hirsch P, Arbyn M, Prendiville W, Mitrou S, Koliopoulos G,
Dalkalitsis N, Stamatopoulos P and Paraskevaidis E: The up-to-date
evidence on colposcopy practice and treatment of cervical
intraepithelial neoplasia: The Cochrane colposcopy and cervical
cytopathology collaborative group (C5 group) approach. Cancer Treat
Rev. 32:516–523. 2006. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
O'Neill E, Reeves MF and Creinin MD:
Baseline colposcopic findings in women entering studies on female
vaginal products. Contraception. 78:162–166. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Waxman AG, Chelmow D, Darragh TM, Lawson H
and Moscicki AB: Revised terminology for cervical histopathology
and its implications for management of high-grade squamous
intraepithelial lesions of the cervix. Obstet Gynecol.
120:1465–1471. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Darragh TM, Colgan TJ, Thomas Cox J,
Heller DS, Henry MR, Luff RD, McCalmont T, Nayar R, Palefsky JM,
Stoler MH, et al: Members of the LAST project work groups. The
lower anogenital squamous terminology standardization project for
HPV-associated lesions: Background and consensus recommendations
from the College of American Pathologists and the American society
for colposcopy and cervical pathology. Int J Gynecol Pathol.
32:76–115. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Burd EM: Human papillomavirus and cervical
cancer. Clin Microbiol Rev. 16:1–17. 2003. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Rumelhart D, Hinton G and Williams R:
Learning representations by back-propagating errors. Nature.
323:533–536. 1986. View
Article : Google Scholar
|
|
13
|
Bengio Y, Courville A and Vincent P:
Representation learning: A review and new perspectives. IEEE Trans
Pattern Anal Mach Intell. 35:1798–1828. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Schmidhuber J: Deep learning in neural
networks: An overview. Neural Netw. 61:85–117. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Srivastava N, Hinton G, Krizhevsky A,
Sutskever I and Salakhutdinov R: Dropout: A simple way to prevent
neural networks from overfitting. J Mach Lean Res. 15:1929–1958.
2014.
|
|
16
|
Nowlan SJ and Hinton GE: Simplifying
neural networks by soft weight-sharing. Neural Comput. 4:473–493.
1992. View Article : Google Scholar
|
|
17
|
Bengio Y: Learning deep architectures for
AI. Foundations and trends® in machine learning.
2:1–127. 2009. View Article : Google Scholar
|
|
18
|
Mutch J and Lowe DG: Object class
recognition and localization using sparse features with limited
receptive fields. Int J Comput Vision. 80:45–57. 2008. View Article : Google Scholar
|
|
19
|
Neal RM: Connectionist learning of belief
networks. Art Intell. 56:71–113. 1992. View Article : Google Scholar
|
|
20
|
Ciresan DC, Meier U, Masci J, Maria
Gambardella L and Schmidhuber J: Flexible, high performance
convolutional neural networks for image classification. IJCAI'11
Proceedings of the Twenty-Second international joint conference on
artificial intelligence. 2:1237–1242. 2011.
|
|
21
|
Scherer D, Müller A and Behnke S:
Evaluation of pooling operations in convolutional architectures for
object recognition. Artificial Neural Networks-ICANN 2010.
Diamantaras K, Duch W and Iliadis LS: Lecture Notes in Computer
Science Springer; Heidelberg: pp. 92–101. 2010, View Article : Google Scholar
|
|
22
|
Huang FJ and LeCun Y: Large-scale learning
with SVM and convolutional for generic object categorization.
Computer Vision and Pattern Recognition, 2006 IEEE Computer Society
Conference. 1:284–291. 2006.
|
|
23
|
Jarrett K, Kavukcuoglu K, Ranzato M and
LeCun Y: What is the best multi-stage architecture for object
recognition? Computer vision. 12th IEEE international conference on
computer vision. 2146–2153. 2009.
|
|
24
|
Zheng Y, Liu Q, Chen E, Ge Y and Zhao JL:
Time series classification using multi-channels deep convolutional
neural networks. Web-Age Information Management. WAIM 2014. Lecture
notes in computer science. Li F, Li G, Hwang S, Yao B and Zhang Z:
Springer; Cham: pp. 298–310. 2014
|
|
25
|
Mnih V, Kavukcuoglu K, Silver D, Rusu AA,
Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK,
Ostrovski G, et al: Human-level control through deep reinforcement
learning. Nature. 518:529–533. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Szegedy C, Liu W, Jia Y, Sermanet P, Reed
S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A: Going deeper
with convolutions. Proceedings of the IEEE conference on computer
vision and pattern recognition. 1–9. 2015.
|
|
27
|
Glorot X, Bordes A and Bengio Y: Deep
sparse rectifier neural networks. Proceedings of the fourteenth
international conference on artificial intelligence and statistics
PMLR. 15:315–323. 2011.
|
|
28
|
Nair V and Hinton G: Rectified linear
units improve restricted Boltzmann machines. Proceedings of the
27th International Conference on Machine Learning Haifa. 807–814.
2010.
|
|
29
|
Ioffe S and Szegedy C: Batch
Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift. 32nd International Conference on Machine
Learning Lille: 2015
|
|
30
|
Krizhevsky A, Sutskever I and Hinton GE:
ImageNet Classification with Deep Convolutional Neural Networks.
25th International Conference on Neural Information Processing
Systems. 1097–1105. 2012.
|
|
31
|
Bridle JS: Probabilistic interpretation of
feedforward classification network outputs, with relationships to
statistical pattern recognition. Neurocomputing. Soulié FF and
Hérault J: Springer; Berlin: pp. 227–236. 1990, View Article : Google Scholar
|
|
32
|
Kohavi R: A study of cross-validation and
bootstrap for accuracy estimation and model selection. Proceedings
of the 14th international joint conference on artificial
intelligence. 2:1137–1143. 1195.
|
|
33
|
Schaffer C: Selecting a classification
method by cross-validation. Mach Lear. 13:135–143. 1993. View Article : Google Scholar
|
|
34
|
Refaeilzadeh P, Tang L and Liu H:
Cross-Validation. Encyclopedia of Database Systems. Liu L and Özsu
MT: Springer; Boston: pp. 532–538. 2009
|
|
35
|
Yu L, Chen H, Dou Q, Qin J and Heng PA:
Automated melanoma recognition in dermoscopy images via very deep
residual networks. IEEE Trans Med Imaging. 36:994–1004. 2017.
View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Caruana R, Lawrence S and Giles CL:
Overfitting in neural nets: Backpropagation, conjugate gradient,
and early stopping. Advances in neural information processing
systems. 13:402–408. 2001.
|
|
37
|
Baum EB and Haussler D: What size net
gives valid generalization? Neural Computation. 1:151–160. 1989.
View Article : Google Scholar
|
|
38
|
Geman S, Bienenstock E and Doursat R:
Neural networks and the bias/variance dilemma. Neural Computation.
4:1–58. 1992. View Article : Google Scholar
|
|
39
|
Krogh A and Hertz JA: A simple weight
decay can improve generalization. In Advances in neural information
processing systems. 4:950–957. 1992.
|
|
40
|
Moody JE: The effective number of
parameters: An analysis of generalization and regularization in
nonlinear learning systems. Advances in Neural Information
Processing Systems. Moody JE, Hanson SJ and Lippmann RP: Morgan
Kaufmann Publishers Inc.; San Francisco, CA: pp. 847–854. 1992
|
|
41
|
Youden WJ: Index for rating diagnostic
tests. Cancer. 3:32–35. 1950. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Cohen J: A coefficient of agreement for
nominal scales. Educ Psychol Meas. 20:37–46. 1960. View Article : Google Scholar
|
|
43
|
McHugh ML: Interrater reliability: The
kappa statistic. Biochem Med (Zagreb). 22:276–282. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
44
|
Poljak M, Kovanda A, Kocjan BJ, Seme K,
Jancar N and Vrtacnik-Bokal E: The abbott RealTime high risk HPV
test: Comparative evaluation of analytical specificity and clinical
sensitivity for cervical carcinoma and CIN 3 lesions with the
Hybrid Capture 2 HPV DNA test. Acta Dermatovenerol Alp Pannonica
Adriat. 18:94–103. 2009.PubMed/NCBI
|
|
45
|
Tjalma WA, Fiander A, Reich O, Powell N,
Nowakowski AM, Kirschner B, Koiss R, O'Leary J, Joura EA, Rosenlund
M, et al: Differences in human papillomavirus type distribution in
high-grade cervical intraepithelial neoplasia and invasive cervical
cancer in Europe. Int J Cancer. 132:854–867. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
46
|
De Sanjose S, Quint WG, Alemany L, Geraets
DT, Klaustermeier JE, Lloveras B, Tous S, Felix A, Bravo LE, Shin
HR, et al: Human papillomavirus genotype attribution in invasive
cervical cancer: A retrospective cross-sectional worldwide study.
Lancet Oncol. 11:1048–1056. 2010. View Article : Google Scholar : PubMed/NCBI
|
|
47
|
Lee SH, Vigliotti JS, Vigliotti VS and
Jones W: From human papillomavirus (HPV) detection to cervical
cancer prevention in clinical practice. Cancers (Basel).
6:2072–2099. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
48
|
Miyagi Y, Fujiwara K, Oda T, Miyake T and
Coleman RL: Development of new method for the prediction of
clinical trial results using compressive sensing of artificial
intelligence. J Biostat Biometric App. 3:2032018.
|
|
49
|
Abbod MF, Catto JW, Linkens DA and Hamdy
FC: Application of artificial intelligence to the management of
urological cancer. J Urol. 178:1150–1156. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
50
|
Litjens G, Sánchez CI, Timofeeva N,
Hermsen M, Nagtegaal I, Kovacs I, Hulsbergen-Van De Kaa C, Bult P,
Van Ginneken B and van der Laak J: Deep learning as a tool for
increased accuracy and efficiency of histopathological diagnosis.
Sci Rep. 6:262862016. View Article : Google Scholar : PubMed/NCBI
|
|
51
|
Khosravi P, Kazemi E, Zhan Q, Toschi M,
Malmsten JE, Hickman C, Meseguer M, Rosenwaks Z, Elemento O,
Zaninovic N and Hajirasouliha I: Robust automated assessment of
human blastocyst quality using deep learning. BioRxiv.
3948822018.
|
|
52
|
Miyagi Y, Habara T, Hirata R and Hayashi
N: Feasibility of deep learning for predicting live birth from a
blastocyst image in patients classified by age. Reprod Med Biol.
18:190–203. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
53
|
Miyagi Y, Habara T, Hirata R and Hayashi
N: Feasibility of artificial intelligence for predicting live birth
without aneuploidy from a blastocyst image. Reprod Med Biol.
18:204–211. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
54
|
Simões PW, Izumi NB, Casagrande RS, Venson
R, Veronezi CD, Moretti GP, da Rocha EL, Cechinel C, Ceretta LB,
Comunello E, et al: Classification of images acquired with
colposcopy using artificial neural networks. Cancer Inform.
13:119–124. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
55
|
Sato M, Horie K, Hara A, Miyamoto Y,
Kurihara K, Tomio K and Yokota H: Application of deep learning to
the classification of images from colposcopy. Oncol Lett.
15:3518–3523. 2018.PubMed/NCBI
|
|
56
|
Ortiz A, Munilla J, Gorriz JM and Ramirez
J: Ensembles of deep learning architectures for the early diagnosis
of the Alzheimer's disease. Int J Neural Syst. 26:16500252016.
View Article : Google Scholar : PubMed/NCBI
|
|
57
|
Gil D, Johnsson M, Chamizo JMG, Paya AS
and Fernandez DR: Application of artificial neural networks in the
diagnosis of urological disfunctions. Expert Syst Appl.
36:5754–5760. 2009. View Article : Google Scholar
|
|
58
|
Olczak J, Fahlberg N, Maki A, Razavian AS,
Jilert A, Stark A, Sköldenberg O and Gordon M: Artificial
intelligence for analyzing orthopedic trauma radiographs. Acta
Orthop. 88:581–586. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
59
|
Sideri M, Garutti P, Costa S, Cristiani P,
Schincaglia P, Sassoli de Bianchi P, Naldoni C and Bucchi L:
Accuracy of colposcopically directed biopsy: Results from an online
quality assurance programme for colposcopy in a population-based
cervical screening setting in Italy. Biomed Res Int.
2015:6140352015. View Article : Google Scholar : PubMed/NCBI
|
|
60
|
Sideri M, Spolti N, Spinaci L, Sanvito F,
Ribaldone R, Surico N and Bucchi L: Interobserver variability of
colposcopic interpretations and consistency with final histologic
results. J Lower Genital Tract Dis. 8:212–216. 2004. View Article : Google Scholar
|
|
61
|
Massad LS, Jeronimo J, Katki HA and
Schiffman M; National Institutes of Health/American Society for
Colposcopy and Cervical Pathology Research Group, : The accuracy of
colposcopic grading for detection of high-grade cervical
intraepithelial neoplasia. J Lower Genital Tract Dis. 13:137–144.
2009. View Article : Google Scholar
|
|
62
|
LeCun Y, Haffner P, Bottou L and Bengio Y:
Object recognition with gradient-based learning. Shape, contour and
grouping in computer vision. Lecture Notes in Computer Science.
Springer, Berlin, Heidelberg. 1681:319–345. 1999.
|
|
63
|
He K, Zhang X, Ren S and Sun J: Deep
residual learning for image recognition. Proceedings of the IEEE
conference on computer vision and pattern recognition. 770–778.
2016.PubMed/NCBI
|
|
64
|
Hu J, Shen L and Sun G:
Squeeze-and-excitation networks. Proceedings of the IEEE conference
on computer vision and pattern recognition. 7132–7141. 2018.
|
|
65
|
Kudva V, Prasad K and Guruvare S:
Automation of detection of cervical cancer using convolutional
neural networks. Crit Rev Biomed Eng. 46:135–145. 2018. View Article : Google Scholar : PubMed/NCBI
|
|
66
|
Esteva A, Kuprel B, Novoa RA, Ko J,
Swetter SM, Blau HM and Thrun S: Dermatologist-level classification
of skin cancer with deep neural networks. Nature. 542:115–118.
2017. View Article : Google Scholar : PubMed/NCBI
|
