View Item 

Show simple item record

Recognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network
dc.contributor.authorJiang, Y.Q.
dc.contributor.authorXiong, J.H.
dc.contributor.authorLi, H.Y.
dc.contributor.authorYang, X.H.
dc.contributor.authorYu, W.T.
dc.contributor.authorGao, M.
dc.contributor.authorZhao, X.
dc.contributor.authorMa, Y.P.
dc.contributor.authorZhang, W.
dc.contributor.authorGuan, Y.F.
dc.contributor.authorGu, H.
dc.contributor.authorSun, J.F.
dc.date.accessioned2020-03-17T18:35:25Z
dc.date.availableWITHHELD_13_MONTHS
dc.date.available2020-03-17T18:35:25Z
dc.date.issued2020-03
dc.identifier.citationJiang, Y.Q.; Xiong, J.H.; Li, H.Y.; Yang, X.H.; Yu, W.T.; Gao, M.; Zhao, X.; Ma, Y.P.; Zhang, W.; Guan, Y.F.; Gu, H.; Sun, J.F. (2020). "Recognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network." British Journal of Dermatology (3): 754-762.
dc.identifier.issn0007-0963
dc.identifier.issn1365-2133
dc.identifier.urihttps://hdl.handle.net/2027.42/154530
dc.publisherWiley Periodicals, Inc.
dc.titleRecognizing basal cell carcinoma on smartphone‐captured digital histopathology images with a deep neural network
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelDermatology
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/154530/1/bjd18026.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/154530/2/bjd18026_am.pdf
dc.identifier.doi10.1111/bjd.18026
dc.identifier.sourceBritish Journal of Dermatology
dc.identifier.citedreferenceKayser K, Görtler J, Bogovac M et al. AI (artificial intelligence) in histopathology – from image analysis to automated diagnosis. Folia Histochem Cytobiol 2009; 47: 355 – 61.
dc.identifier.citedreferenceWeaver DL, Krag DN, Manna EA et al. Comparison of pathologist‐detected and automated computer‐assisted image analysis detected sentinel lymph node micrometastases in breast cancer. Mod Pathol 2003; 16: 1159 – 63.
dc.identifier.citedreferenceGandomkar Z, Brennan PC, Mello‐Thoms C. Computer‐based image analysis in breast pathology. J Pathol Inform 2016; 7: 43.
dc.identifier.citedreferenceHe K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Available at: https://arxiv.org/abs/1512.03385 (last accessed 5 June 2019).
dc.identifier.citedreferenceSun Y, Liang D, Wang X, Tang X. Deepid3: face recognition with very deep neural networks. Available at: https://arxiv.org/abs/1502.00873 (last accessed 5 June 2019).
dc.identifier.citedreferenceRen S, He K, Girshick R, Sun J. Faster R‐CNN: towards real‐time object detection with region proposal networks. Available at: https://ieeexplore.ieee.org/document/7485869 (last accessed 5 June 2019).
dc.identifier.citedreferenceHe K, Gkioxari G, Dollár P, Girshick R. Mask R‐CNN. Available at: https://arxiv.org/abs/1703.06870 (last accessed 5 June 2019).
dc.identifier.citedreferenceLong J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Available at: https://arxiv.org/abs/1411.4038 (last accessed 5 June 2019).
dc.identifier.citedreferencePinchaud N, Hedlund M. CAMELYON17 Grand Challenge. Available at: https://camelyon17.grand-challenge.org (last accessed 5 June 2019).
dc.identifier.citedreferenceWang D, Khosla A, Gargeya R et al. Deep learning for identifying metastatic breast cancer. Available at: https://arxiv.org/abs/1606.05718 (last accessed 5 June 2019).
dc.identifier.citedreferenceLiu Y, Gadepalli K, Norouzi M et al. Detecting cancer metastases on gigapixel pathology images. Available at: https://arxiv.org/abs/1703.02442 (last accessed 5 June 2019).
dc.identifier.citedreferenceZhou C, Yu Y, Xue R, Elston DM. High‐quality digital photomicrography utilizing a smartphone without adapter. J Cutan Pathol 2016; 43: 82 – 4.
dc.identifier.citedreferenceSzegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision. Available at: https://arxiv.org/abs/1512.00567 (last accessed 5 June 2019).
dc.identifier.citedreferenceLin M, Chen Q, Yan S. Network in network. Available at: https://arxiv.org/abs/1312.4400 (last accessed 5 June 2019).
dc.identifier.citedreferenceDeng J, Dong W, Socher R et al. ImageNet: a large‐scale hierarchical image database. Available at: https://ieeexplore.ieee.org/document/5206848 (last accessed 5 June 2019).
dc.identifier.citedreferenceJia Y, Shelhamer E, Donahue J et al. Caffe: convolutional architecture for fast feature embedding. Available at: https://arxiv.org/abs/1408.5093 (last accessed 5 June 2019).
dc.identifier.citedreferenceChen LC, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. Available at: https://arxiv.org/abs/1706.05587 (last accessed 5 June 2019).
dc.identifier.citedreferenceAbadi M, Barham P, Chen J et al. Tensorflow: a system for large‐scale machine learning. Available at: https://arxiv.org/abs/1605.08695v2 (last accessed 5 June 2019).
dc.identifier.citedreferenceCireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. Med Image Comput Comput Assist Interv 2013; 16: 411 – 18.
dc.identifier.citedreferenceVeta M, van Diest PJ, Jiwa M et al. Mitosis counting in breast cancer: object‐level interobserver agreement and comparison to an automatic method. PLOS ONE 2016; 11: e0161286.
dc.identifier.citedreferenceEhteshami Bejnordi B, Veta M, Johannes van Diest P et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017; 318: 2199 – 210.
dc.identifier.citedreferenceLitjens G, Sánchez CI, Timofeeva N et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 2016; 6: 26286.
dc.identifier.citedreferenceGoyal M, Yap MH. Region of interest detection in dermoscopic images for natural data‐augmentation. Available at: https://arxiv.org/abs/1807.10711 (last accessed 5 June 2019).
dc.identifier.citedreferenceCruz‐Roa AA, Arevalo Ovalle JE, Madabhushi A, González Osorio FA. A deep learning architecture for image representation, visual interpretability and automated basal‐cell carcinoma cancer detection. Med Image Comput Comput Assist Interv 2013; 16: 403 – 10.
dc.identifier.citedreferenceNavid F, Anil VP, Liron P. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol Lab Med Int 2015; 7: 23 – 33.
dc.identifier.citedreferenceGoldenberg G, Karagiannis T, Palmer JB et al. Incidence and prevalence of basal cell carcinoma (BCC) and locally advanced BCC (LABCC) in a large commercially insured population in the United States: a retrospective cohort study. J Am Acad Dermatol 2016; 75: 957 – 66.
dc.identifier.citedreferenceShi Y, Jia R, Fan X. Ocular basal cell carcinoma: a brief literature review of clinical diagnosis and treatment. Onco Targets Ther 2017; 10: 2483 – 9.
dc.identifier.citedreferenceGhaznavi F, Evans A, Madabhushi A, Feldman M. Digital imaging in pathology: whole‐slide imaging and beyond. Annu Rev Pathol 2013; 8: 331 – 59.
dc.identifier.citedreferenceGurcan MN, Boucheron LE, Can A et al. Histopathological image analysis: a review. IEEE Rev Biomed Eng 2009; 2: 147 – 71.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
bjd18026.pdf
Size:
1.343MB
Format:
PDF
View/Open
Name:
bjd18026_am.pdf
Size:
471.9KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.