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Items: 1 to 20 of 102

1.

Computational Optics Enables Breast Cancer Profiling in Point-of-Care Settings.

Min J, Im H, Allen M, McFarland PJ, Degani I, Yu H, Normandin E, Pathania D, Patel JM, Castro CM, Weissleder R, Lee H.

ACS Nano. 2018 Sep 25;12(9):9081-9090. doi: 10.1021/acsnano.8b03029. Epub 2018 Aug 20.

2.

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

Hinton B, Ma L, Mahmoudzadeh AP, Malkov S, Fan B, Greenwood H, Joe B, Lee V, Kerlikowske K, Shepherd J.

Cancer Imaging. 2019 Jun 22;19(1):41. doi: 10.1186/s40644-019-0227-3.

3.

Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer.

Steiner DF, MacDonald R, Liu Y, Truszkowski P, Hipp JD, Gammage C, Thng F, Peng L, Stumpe MC.

Am J Surg Pathol. 2018 Dec;42(12):1636-1646. doi: 10.1097/PAS.0000000000001151.

4.

Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates.

Wolberg WH, Street WN, Mangasarian OL.

Cancer Lett. 1994 Mar 15;77(2-3):163-71.

PMID:
8168063
5.

Deep learning for image analysis: Personalizing medicine closer to the point of care.

Xie Q, Faust K, Van Ommeren R, Sheikh A, Djuric U, Diamandis P.

Crit Rev Clin Lab Sci. 2019 Jan;56(1):61-73. doi: 10.1080/10408363.2018.1536111. Epub 2019 Jan 10. Review.

PMID:
30628494
6.

Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches.

Miao S, Xu T, Wu Y, Xie H, Wang J, Jing S, Zhang Y, Zhang X, Yang Y, Zhang X, Shan T, Wang L, Xu H, Wang S, Liu Y.

Int J Med Inform. 2018 Nov;119:17-21. doi: 10.1016/j.ijmedinf.2018.08.009. Epub 2018 Aug 18.

PMID:
30342682
7.

Digital image analysis in breast pathology-from image processing techniques to artificial intelligence.

Robertson S, Azizpour H, Smith K, Hartman J.

Transl Res. 2018 Apr;194:19-35. doi: 10.1016/j.trsl.2017.10.010. Epub 2017 Nov 7. Review.

PMID:
29175265
8.

Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm.

Jeyaraj PR, Samuel Nadar ER.

J Cancer Res Clin Oncol. 2019 Apr;145(4):829-837. doi: 10.1007/s00432-018-02834-7. Epub 2019 Jan 3.

PMID:
30603908
9.

Deep learning in mammography and breast histology, an overview and future trends.

Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R.

Med Image Anal. 2018 Jul;47:45-67. doi: 10.1016/j.media.2018.03.006. Epub 2018 Mar 26. Review.

PMID:
29679847
10.

Computer-aided diagnosis of breast cancer using cytological images: A systematic review.

Saha M, Mukherjee R, Chakraborty C.

Tissue Cell. 2016 Oct;48(5):461-74. doi: 10.1016/j.tice.2016.07.006. Epub 2016 Jul 30. Review.

PMID:
27528421
11.

Comparative assessment of CNN architectures for classification of breast FNAC images.

Saikia AR, Bora K, Mahanta LB, Das AK.

Tissue Cell. 2019 Apr;57:8-14. doi: 10.1016/j.tice.2019.02.001. Epub 2019 Feb 5.

PMID:
30947968
12.

Automated algorithm for differentiation of human breast tissue using low coherence interferometry for fine needle aspiration biopsy guidance.

Goldberg BD, Iftimia NV, Bressner JE, Pitman MB, Halpern E, Bouma BE, Tearney GJ.

J Biomed Opt. 2008 Jan-Feb;13(1):014014. doi: 10.1117/1.2837433.

13.

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.

Nahid AA, Kong Y.

Comput Math Methods Med. 2017;2017:3781951. doi: 10.1155/2017/3781951. Epub 2017 Dec 31. Review.

14.

Digital image analysis of breast epithelial cells collected by random periareolar fine-needle aspirates (RPFNA) from women at high risk for breast cancer taking hormone replacement and the aromatase inhibitor, letrozole, for six months.

Frank DH, Kimler BF, Fabian CJ, Ranger-Moore J, Yozwiak M, Bartels HG, Alberts DS, Bartels PH.

Breast Cancer Res Treat. 2009 Jun;115(3):661-8. doi: 10.1007/s10549-008-0274-0. Epub 2009 Jan 6.

PMID:
19125322
15.

Computerized breast cancer diagnosis and prognosis from fine-needle aspirates.

Wolberg WH, Street WN, Heisey DM, Mangasarian OL.

Arch Surg. 1995 May;130(5):511-6.

PMID:
7748089
16.

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Yousefi M, Krzy┼╝ak A, Suen CY.

Comput Biol Med. 2018 May 1;96:283-293. doi: 10.1016/j.compbiomed.2018.04.004. Epub 2018 Apr 12.

PMID:
29665537
17.

Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography.

Butola A, Ahmad A, Dubey V, Srivastava V, Qaiser D, Srivastava A, Senthilkumaran P, Mehta DS.

Appl Opt. 2019 Feb 10;58(5):A135-A141. doi: 10.1364/AO.58.00A135.

PMID:
30873970
18.

Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms.

Cai H, Huang Q, Rong W, Song Y, Li J, Wang J, Chen J, Li L.

Comput Math Methods Med. 2019 Mar 3;2019:2717454. doi: 10.1155/2019/2717454. eCollection 2019.

19.

Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images.

Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R.

Comput Biol Med. 2013 Oct;43(10):1563-72. doi: 10.1016/j.compbiomed.2013.08.003. Epub 2013 Aug 19.

PMID:
24034748
20.

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis.

Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Richter C, Cha K.

Phys Med Biol. 2018 May 1;63(9):095005. doi: 10.1088/1361-6560/aabb5b.

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