Abstract
Machine learning (ML) is a form of artificial intelligence which is placed to transform the twenty-first century. Rapid, recent progress in its underlying architecture and algorithms and growth in the size of datasets have led to increasing computer competence across a range of fields. These include driving a vehicle, language translation, chatbots and beyond human performance at complex board games such as Go. Here, we review the fundamentals and algorithms behind machine learning and highlight specific approaches to learning and optimisation. We then summarise the applications of ML to medicine. In particular, we showcase recent diagnostic performances, and caveats, in the fields of dermatology, radiology, pathology and general microscopy.
References
Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Seung HS (2017) Trainable Weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33(15):2424–2426. https://doi.org/10.1093/bioinformatics/btx180
Belevich I, Joensuu M, Kumar D, Vihinen H, Jokitalo E (2016) Microscopy image browser: a platform for segmentation and analysis of multidimensional datasets. PLoS Biol 14(1):1–13. https://doi.org/10.1371/journal.pbio.1002340
Bottou L (2010) Large-scale machine learning with stochastic gradient descent. Proceedings of COMPSTAT’2010, 177–186. doi: https://doi.org/10.1007/978-3-7908-2604-3_16
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140. https://doi.org/10.1007/BF00058655
Cabitza F, Rasoini R, Gensini GF (2017) Unintended consequences of machine learning in medicine. JAMA. https://doi.org/10.1001/jama.2017.7797
Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N (2015) Intelligible models for healthcare. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD’15 (pp. 1721–1730). doi: https://doi.org/10.1145/2783258.2788613
Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, … Tang A (2017) Deep learning: a primer for radiologists. RadioGraphics 37(7):2113–2131.https://doi.org/10.1148/rg.2017170077
Deng J, Dong W, Socher R, Li L-J, Li K, Li F-F (2009) ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). doi https://doi.org/10.1109/CVPRW.2009.5206848
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, … Venâncio R (2017) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22):2199. doi https://doi.org/10.1001/jama.2017.14585
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118. https://doi.org/10.1038/nature21056
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, … Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410. doi:https://doi.org/10.1001/jama.2016.17216
Hall MA, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18. https://doi.org/10.1145/1656274.1656278
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer New York, New York
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In Appears in the International Joint Conference on Articial Intelligence (IJCAI), pp. 1–7. doi https://doi.org/10.1067/mod.2000.109031
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst:1–9. https://doi.org/10.1016/j.protcy.2014.09.007
LeCun Y (1988) A theoretical framework for back-propagation. Proceedings of the 1988 connectionist models summer school. doi https://doi.org/10.1007/978-3-642-35289-8
Litjens G, Sánchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I, … Van Der Laak J (2016) Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 6:1–11. doi https://doi.org/10.1038/srep26286
Litjens G, Kooi T, Bejnordi BE, Arindra A, Setio A, Ciompi F et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005
Marchetti, M. A., Codella, N. C. F., Dusza, S. W., Gutman, D. A., Helba, B., Kalloo, A., … Halpern, A. C. (2018). Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. doi https://doi.org/10.1016/j.jaad.2017.08.016
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2009.191
Parker DB (1985) Learning-logic: casting the cortex of the human brain in silicon Technical report Tr-47, Centre for computational research in economics and management science. MIT, Cambridge
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536. https://doi.org/10.1038/323533a0
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S et al (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252. https://doi.org/10.1007/s11263-015-0816-y
Schindelin, J et al (2012) Fiji: an open-source platform for biological-image analysis, Nature methods 9(7):676–682
Silver D et al (2017) Mastering the game of go without human knowledge. Nature 550(7676):354–359
Spontón H, Cardelino J (2015) A review of classic edge detectors. IPOL 5:90–123. https://doi.org/10.5201/ipol.2015.35
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958. https://doi.org/10.1214/12-AOS1000
Staniewicz L, Midgley PA (2015) Machine learning as a tool for classifying electron tomographic reconstructions. Adv Struct Chem Imaging 1(1):9. https://doi.org/10.1186/s40679-015-0010-x
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. https://doi.org/10.1109/CVPR.2016.308
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
All authors declare that they have no conflicts of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
This article is part of a Special Issue on ‘Big Data’ edited by Joshua WK Ho and Eleni Giannoulatou
Rights and permissions
About this article
Cite this article
Nichols, J.A., Herbert Chan, H.W. & Baker, M.A.B. Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophys Rev 11, 111–118 (2019). https://doi.org/10.1007/s12551-018-0449-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12551-018-0449-9