|
1
|
Noda K, Arie H, Suga Y and Ogata T:
Multimodal integration learning of robot behavior using deep neural
networks. Robot Auton Syst. 62:721–736. 2014. View Article : Google Scholar
|
|
2
|
Chen JH and Asch SM: Machine Learning and
Prediction in Medicine - Beyond the Peak of Inflated Expectations.
N Engl J Med. 376:2507–2509. 2017.PubMed/NCBI View Article : Google Scholar
|
|
3
|
Ehteshami Bejnordi B, Veta M, Johannes van
Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak
JA, Hermsen M, Manson QF, Balkenhol M, et al: the CAMELYON16
Consortium: Diagnostic assessment of deep learning algorithms for
detection of lymph node metastases in women with breast cancer.
JAMA. 318:2199–2210. 2017.PubMed/NCBI View Article : Google Scholar
|
|
4
|
Madani A, Ong JR, Tibrewal A and Mofrad
MRK: Deep echocardiography: Data-efficient supervised and
semi-supervised deep learning towards automated diagnosis of
cardiac disease. NPJ Digit Med. 1(59)2018. View Article : Google Scholar
|
|
5
|
Ding Y, Sohn JH, Kawczynski MG, Trivedi H,
Harnish R, Jenkins NW, Lituiev D, Copeland TP, Aboian MS, Mari
Aparici C, et al: A Deep learning model to predict a diagnosis of
alzheimer disease by using 18F-FDG PET of the brain.
Radiology. 290:456–464. 2019.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang
B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP, et al: Deep
learning for chest radiograph diagnosis: A retrospective comparison
of the CheXNeXt algorithm to practicing radiologists. PLoS Med.
15(e1002686)2018.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Causey JL, Zhang J, Ma S, Jiang B, Qualls
JA, Politte DG, Prior F, Zhang S and Huang X: Highly accurate model
for prediction of lung nodule malignancy with CT scans. Sci Rep.
8(9286)2018.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Li Z, Wang Y, Yu J, Guo Y and Cao W: Deep
Learning based Radiomics (DLR) and its usage in noninvasive IDH1
prediction for low grade glioma. Sci Rep. 7(5467)2017.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Akkus Z, Ali I, Sedlář J, Agrawal JP,
Parney IF, Giannini C and Erickson BJ: Predicting deletion of
chromosomal arms 1p/19q in low-Grade gliomas from MR images using
machine intelligence. J Digit Imaging. 30:469–476. 2017.PubMed/NCBI View Article : Google Scholar
|
|
10
|
Bibault JE, Giraud P, Housset M, Durdux C,
Taieb J, Berger A, Coriat R, Chaussade S, Dousset B, Nordlinger B,
et al: Deep Learning and Radiomics predict complete response after
neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci
Rep. 8(12611)2018.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Yousefi S, Amrollahi F, Amgad M, Dong C,
Lewis JE, Song C, Gutman DA, Halani SH, Velazquez Vega JE, Brat DJ,
et al: Predicting clinical outcomes from large scale cancer genomic
profiles with deep survival models. Sci Rep.
7(11707)2017.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Matsuo K, Purushotham S, Jiang B,
Mandelbaum RS, Takiuchi T, Liu Y and Roman LD: Survival outcome
prediction in cervical cancer: Cox models vs deep-learning model.
Am J Obstet Gynecol. Dec 21, 2018 (Epub ahead of print).
2018.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Miotto R, Li L, Kidd BA and Dudley JT:
Deep Patient: An Unsupervised representation to predict the future
of patients from the electronic health records. Sci Rep.
6(26094)2016.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Chen H, Engkvist O, Wang Y, Olivecrona M
and Blaschke T: The rise of deep learning in drug discovery. Drug
Discov Today. 23:1241–1250. 2018.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Ong BT, Sugiura K and Zettsu K:
Dynamically pre-trained deep recurrent neural networks using
environmental monitoring data for predicting PM2.5. Neural Comput
Appl. 27:1553–1566. 2016.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Shwartz-Ziv R and Tishby N: Opening the
Black Box of Deep Neural Networks via Information. Intel
Collaborative Research institute for Computational Intelligence
(ICRI-CI). arXiv:1703.00810. 2017.
|
|
17
|
Tishby N and Zaslavsky N: Deep learning
and the information bottleneck principle. In 2015 IEEE Information
Theory Workshop (ITW). pp1-5. 2015. View Article : Google Scholar
|
