Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction

BMC Bioinformatics

Volumn 19, Issue 1, 2018, Pages

  Faust, Kevin ^a   Xie, Quin ^b   Han, Dominick ^a   Goyle, Kartikay ^a   Volynskaya, Zoya ^b,c   Djuric, Ugljesa ^c,d   Diamandis, Phedias ^b,c,d  

^a UNIVERSITY OF TORONTO   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c UNIVERSITY HEALTH NETWORK   (Canada)

^d PRINCESS MARGARET HOSPITAL   (Canada)

Author keywords

Artificial intelligence; Cancer; Convolutional neural networks; Deep learning; Diagnostics; Digital pathology; Glioblastoma; Machine learning; Neuropathology; T SNE

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CONVOLUTION; DECISION MAKING; LEARNING SYSTEMS; NEURAL NETWORKS; PLASMA DIAGNOSTICS; PROBABILITY DISTRIBUTIONS; STOCHASTIC SYSTEMS;

CANCER; CONVOLUTIONAL NEURAL NETWORK; DIGITAL PATHOLOGIES; GLIOBLASTOMAS; NEUROPATHOLOGY; T-SNE;

DEEP LEARNING;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; HUMAN; MACHINE LEARNING; STANDARDS;

ARTIFICIAL INTELLIGENCE; DEEP LEARNING; HUMANS; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER);

EID: 85047079043     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-018-2184-4     Document Type: Article

References (22)

1. Djuric U, Zadeh G, Aldape K, Diamandis P. Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care. npj Precis. Oncol. [Internet]. 2017 [cited 2017 Aug 29];1. Available from: https://www.nature.com/articles/s41698-017-0022-1.pdf
2. Liu Y, Gadepalli K, Norouzi M, Dahl GE, Kohlberger T, Boyko A, et al. Detecting Cancer Metastases on Gigapixel Pathology Images. 2017. Available from: http://arxiv.org/abs/1703.02442
3. Litjens G, Sánchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. [Internet]. 2016;6:26286. Available from: http://www.nature.com/articles/srep26286.
4. Gibney E. Google AI algorithm masters ancient game of Go. Nature [Internet]. 2016 [cited 2017 Feb 26];529:445-6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26819021
5. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, et al. Mastering the game of Go with deep neural networks and tree search. Nature [Internet]. 2016 [cited 2017 Feb 26];529:484-9. Available from: http://www.nature.com/doifinder/10.1038/nature16961.
6. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature [Internet]. 2017 [cited 2017 Feb 26];542:115-8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28117445.
7. Yu K-H, Zhang C, Berry GJ, Altman RB, Ré C, Rubin DL, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. [Internet]. 2016;7:12474. Available from: http://www.nature.com/doifinder/10.1038/ncomms12474.
8. Wang H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, et al. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J. Med. Imaging [Internet]. 2014;1:34003. Available from: http://medicalimaging.spiedigitallibrary.org/article.aspx?doi=10.1117/1.JMI.1.3.034003.
9. Barker J, Hoogi A, Depeursinge A, Rubin DL. Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med. Image Anal. [Internet]. 2016;30:60-71. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1361841515001838
10. Xu Y, Jia Z, Wang L-B, Ai Y, Zhang F, Lai M, et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics [Internet]. 2017;18:281. Available from: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1685-x
11. Van Der Maaten L, Hinton G. Visualizing Data using t-SNE. J. Mach. Learn. Res. [Internet]. 2008;9:2579-605. Available from: http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
12. Jamieson AR, Giger ML, Drukker K, Li H, Yuan Y, Bhooshan N. Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t-SNE. Med. Phys. [Internet]. American Association of Physicists in Medicine, 2009;37:339-51. Available from: http://doi.wiley.com/10.1118/1.3267037
13. Wallach I, Lilien R. The protein-small-molecule database, a non-redundant structural resource for the analysis of protein-ligand binding. Bioinformatics [Internet]. 2009;25:615-20. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19153135
14. Birjandtalab J, Pouyan MB, Nourani M. Nonlinear dimension reduction for EEG-based epileptic seizure detection. 2016 IEEE-EMBS Int. Conf. Biomed. Heal. Informatics [Internet]. IEEE; 2016 [cited 2017 Sep 11]. p. 595-8. Available from: http://ieeexplore.ieee.org/document/7455968/.
15. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131(6):803-20.
16. Ostrom QT, Gittleman H, Liao P, Rouse C, Chen Y, Dowling J, et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007-2011. Neuro-Oncology. 2014;16(Suppl 4):iv1-63.
17. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. 2014. Available from: http://arxiv.org/abs/1409.1556
18. Liu Y, Gadepalli K, Norouzi M, Dahl GE, Kohlberger T, Boyko A, et al. Detecting Cancer metastases on Gigapixel pathology images. 2017;
19. Nakhleh RE. Patient safety and error reduction in surgical pathology. Arch Pathol Lab Med. 2008;132:181-5.
20. Nakhleh RE. Role of informatics in patient safety and quality assurance. Surg Pathol Clin. 2015;8:301-7.
21. Landgrebe TCW, Duin RPW. Approximating the multiclass ROC by pairwise analysis. 2007. Available from: https://www.sciencedirect.com/science/article/pii/S016786550700150X
22. Rauber PE, Fadel SG, Falcao AX, Telea AC. Visualizing the Hidden Activity of Artificial Neural Networks. IEEE Trans. Vis. Comput. Graph. [Internet]. 2017;23:101-10. Available from: http://ieeexplore.ieee.org/document/7539329/