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Items: 1 to 20 of 105

1.

Machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification.

Yates EJ, Yates LC, Harvey H.

Clin Radiol. 2018 Sep;73(9):827-831. doi: 10.1016/j.crad.2018.05.015. Epub 2018 Jun 10.

PMID:
29898829
2.

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

Rajkomar A, Lingam S, Taylor AG, Blum M, Mongan J.

J Digit Imaging. 2017 Feb;30(1):95-101. doi: 10.1007/s10278-016-9914-9.

3.

Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study.

Taylor AG, Mielke C, Mongan J.

PLoS Med. 2018 Nov 20;15(11):e1002697. doi: 10.1371/journal.pmed.1002697. eCollection 2018 Nov.

4.
5.

Re: machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification.

Halligan S, Plumb AAO.

Clin Radiol. 2019 Feb;74(2):161. doi: 10.1016/j.crad.2018.11.010. Epub 2018 Dec 15. No abstract available.

PMID:
30563699
6.

Re: machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification. A reply.

Yates EJ, Yates LC, Harvey H.

Clin Radiol. 2019 Feb;74(2):162. doi: 10.1016/j.crad.2018.11.008. Epub 2018 Dec 15. No abstract available.

PMID:
30563698
7.

Re: machine learning "red dot": open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification. Editor's reply.

Weston M.

Clin Radiol. 2019 Feb;74(2):163. doi: 10.1016/j.crad.2018.12.001. Epub 2018 Dec 24. No abstract available.

PMID:
30591212
8.

Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks.

Kim DH, MacKinnon T.

Clin Radiol. 2018 May;73(5):439-445. doi: 10.1016/j.crad.2017.11.015. Epub 2017 Dec 18.

PMID:
29269036
9.

Machine Learning Interface for Medical Image Analysis.

Zhang YC, Kagen AC.

J Digit Imaging. 2017 Oct;30(5):615-621. doi: 10.1007/s10278-016-9910-0.

10.

Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

Cicero M, Bilbily A, Colak E, Dowdell T, Gray B, Perampaladas K, Barfett J.

Invest Radiol. 2017 May;52(5):281-287. doi: 10.1097/RLI.0000000000000341.

PMID:
27922974
11.

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP, Patel BN, Yeom KW, Shpanskaya K, Blankenberg FG, Seekins J, Amrhein TJ, Mong DA, Halabi SS, Zucker EJ, Ng AY, Lungren MP.

PLoS Med. 2018 Nov 20;15(11):e1002686. doi: 10.1371/journal.pmed.1002686. eCollection 2018 Nov.

12.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

Pang S, Yu Z, Orgun MA.

Comput Methods Programs Biomed. 2017 Mar;140:283-293. doi: 10.1016/j.cmpb.2016.12.019. Epub 2017 Jan 6.

PMID:
28254085
13.

Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.

Treder M, Lauermann JL, Eter N.

Graefes Arch Clin Exp Ophthalmol. 2018 Nov;256(11):2053-2060. doi: 10.1007/s00417-018-4098-2. Epub 2018 Aug 8.

PMID:
30091055
14.

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Lakhani P, Sundaram B.

Radiology. 2017 Aug;284(2):574-582. doi: 10.1148/radiol.2017162326. Epub 2017 Apr 24.

PMID:
28436741
15.

OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications.

Prahs P, Radeck V, Mayer C, Cvetkov Y, Cvetkova N, Helbig H, Märker D.

Graefes Arch Clin Exp Ophthalmol. 2018 Jan;256(1):91-98. doi: 10.1007/s00417-017-3839-y. Epub 2017 Nov 10.

PMID:
29127485
16.

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Singh V, Danda V, Gorniak R, Flanders A, Lakhani P.

J Digit Imaging. 2019 May 9. doi: 10.1007/s10278-019-00229-9. [Epub ahead of print]

PMID:
31073816
17.

Deep Learning to Classify Radiology Free-Text Reports.

Chen MC, Ball RL, Yang L, Moradzadeh N, Chapman BE, Larson DB, Langlotz CP, Amrhein TJ, Lungren MP.

Radiology. 2018 Mar;286(3):845-852. doi: 10.1148/radiol.2017171115. Epub 2017 Nov 13.

PMID:
29135365
18.

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.

Coy H, Hsieh K, Wu W, Nagarajan MB, Young JR, Douek ML, Brown MS, Scalzo F, Raman SS.

Abdom Radiol (NY). 2019 Jun;44(6):2009-2020. doi: 10.1007/s00261-019-01929-0.

PMID:
30778739
19.
20.

Prediction of Response to Stereotactic Radiosurgery for Brain Metastases Using Convolutional Neural Networks.

Cha YJ, Jang WI, Kim MS, Yoo HJ, Paik EK, Jeong HK, Youn SM.

Anticancer Res. 2018 Sep;38(9):5437-5445. doi: 10.21873/anticanres.12875.

PMID:
30194200

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