Developments in AI and Machine Learning for Neuroimaging

Part of the Lecture Notes in Computer Science book series (LNCS, volume 12090)


This paper reviews guidelines on how medical imaging analysis can be enhanced by Artificial Intelligence (AI) and Machine Learning (ML). In addition to outlining current and potential future developments, we also provide background information on chemical imaging and discuss the advantages of Explainable AI. We hypothesize that it is a matter of AI to find an invariably recurring parameter that has escaped human attention (e.g. due to noisy data). There is great potential in AI to illuminate the feature space of successful models.


Explainable AI Stereology Neurodegenerative diseases Neuroimaging Disector 7 T post-mortem MRI Brain mapping 


  1. 1.
    Amoroso, N., et al.: Brain structural connectivity atrophy in Alzheimer’s disease. arXiv (2017)Google Scholar
  2. 2.
    Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning - a new frontier in artificial intelligence research - research frontier. IEEE Comput. Intell. Mag. 5, 4 (2010). Scholar
  3. 3.
    Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one 10(7), e0130140 (2015)CrossRefGoogle Scholar
  4. 4.
    Bartelle, B.B., Barandov, A., Jasanoff, A.: Molecular fMRI. J. Neurosci. 36(15), 4139–4148 (2016)CrossRefGoogle Scholar
  5. 5.
    Baxt, W.G.: Use of an artificial neural network for the diagnosis of myocardial infarction. Ann. Intern. Med. 115, 11 (1991)CrossRefGoogle Scholar
  6. 6.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  7. 7.
    Breiman, L.: Random forests. Mach. Learn. 45, 1 (2001). Scholar
  8. 8.
    Bult, C.J.: Mouse tumor biology (MTB): a database of mouse models for human cancer. Nucleic Acids Res. 43, D818–D824 (2014). Scholar
  9. 9.
    Calhoun, V.D., Sui, J.: Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 1(3), 230–244 (2016)CrossRefGoogle Scholar
  10. 10.
    Daffner, K.: Current approaches to the clinical diagnosis of Alzheimer’s disease. In: Scinto, L.F.M., Daffner, K.R. (eds.) Early Diagnosis of Alzheimer’s Disease. Current Clinical Neurology, pp. 29–64. Humana Press, Totowa (2000).
  11. 11.
    Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 10 (2000)CrossRefGoogle Scholar
  12. 12.
    Gardiner, S.L., van Belzen, M.J., Boogaard, M.W., et al.: Huntingtin gene repeat size variations affect risk of lifetime depression. Transl. Psychiatry 7, 1277 (2017). Scholar
  13. 13.
    Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. arXiv preprint arXiv:1806.00069 (2018)
  14. 14.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  15. 15.
    Greenspan, H., Van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35, 5 (2016)CrossRefGoogle Scholar
  16. 16.
    Gupta, A., Ayhan, M., Maida, A.: In natural image bases to represent neuroimaging data. In: International Conference on Machine Learning, pp. 987–994 (2013)Google Scholar
  17. 17.
    Hadzi, T.C., et al.: Assessment of cortical and striatal involvement in 523 Huntington disease brains. Neurology 79, 1708–1715 (2012)CrossRefGoogle Scholar
  18. 18.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)Google Scholar
  19. 19.
    Heim, B., et al.: Magnetic resonance imaging for the diagnosis of Parkinson’s disease. J. Neural Transm. 124, 8 (2017)CrossRefGoogle Scholar
  20. 20.
    Holzinger, A.: Introduction to machine learning and knowledge extraction (make). Mach. Learn. Knowl. Extr. 1(1), 1–20 (2017)CrossRefGoogle Scholar
  21. 21.
    Holzinger, A., et al.: Machine learning and knowledge extraction in digital pathology needs an integrative approach. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds.) Towards Integrative Machine Learning and Knowledge Extraction. LNCS (LNAI), vol. 10344, pp. 13–50. Springer, Cham (2017).
  22. 22.
    Holzinger, A., Carrington, A., Müller, H.: Measuring the quality of explanations: The system causability scale (scs). Comparing human and machine explanations. KI - Künstliche Intelligenz (German J. Artif. Intell.). Special Issue on Interactive Machine Learning, Edited by Kristian Kersting, TU Darmstadt 34(2) (2020).
  23. 23.
    Holzinger, A., Goebel, R., Palade, V., Ferri, M.: Towards integrative machine learning and knowledge extraction. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds.) Towards Integrative Machine Learning and Knowledge Extraction. LNCS (LNAI), vol. 10344, pp. 1–12. Springer, Cham (2017).
  24. 24.
    Holzinger, A., Kieseberg, P., Weippl, E., Tjoa, A.M.: Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2018. LNCS, vol. 11015, pp. 1–8. Springer, Cham (2018).
  25. 25.
    Holzinger, A., Langs, G., Denk, H., Zatloukal, K., Mueller, H.: Causability and explainability of artificial intelligence in medicine. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 9(4), e1312 (2019). Scholar
  26. 26.
    Hyder, F., Rothman, D.: Advances in imaging brain metabolism. Ann. Rev. Biomed. Eng. 19, 485–515 (2017)CrossRefGoogle Scholar
  27. 27.
    Jeanquartier, F., et al.: Machine learning for in silico modeling of tumor growth. In: Holzinger, A. (ed.) Machine Learning for Health Informatics. LNCS (LNAI), vol. 9605, pp. 415–434. Springer, Cham (2016).
  28. 28.
    Jeanquartier, F., Jean-Quartier, C., Schreck, T., Cemernek, D., Holzinger, A.: Integrating open data on cancer in support to tumor growth analysis. In: Renda, M.E., Bursa, M., Holzinger, A., Khuri, S. (eds.) ITBAM 2016. LNCS, vol. 9832, pp. 49–66. Springer, Cham (2016).
  29. 29.
    Job, D.E., et al.: A brain imaging repository of normal structural MRI across the life course: brain images of normal subjects (brains). NeuroImage 144, 299–304 (2017)CrossRefGoogle Scholar
  30. 30.
    Klein, M., et al.: Brain imaging genetics in ADHD and beyond- mapping pathways from gene to disorder at different levels of complexity. Neurosci. Biobehav. Rev. 80, 115–155 (2017). Scholar
  31. 31.
    Klöppel, S., Abdulkadir, A., Jack, C.R., Koutsouleris, N., Mourāo-Miranda, J., Vemuri, P.: Diagnostic neuroimaging across diseases. Neuroimage 61(2), 457–463 (2012)CrossRefGoogle Scholar
  32. 32.
    Lakkaraju, H., Kamar, E., Caruana, R., Leskovec, J.: Interpretable and explorable approximations of black box models. arXiv preprint arXiv:1707.01154 (2017)
  33. 33.
    Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 7553 (2015). Scholar
  34. 34.
    Lee, T., Cai, L.X., Lelyveld, V.S., Hai, A., Jasanoff, A.: Molecular-level functional magnetic resonance imaging of dopaminergic signaling. Science 344(6183), 533–535 (2014)CrossRefGoogle Scholar
  35. 35.
    Lemm, S., Blankertz, B., Dickhaus, T., Mueller, K.R.: Introduction to machine learning for brain imaging. Neuroimage 10(1016), 387–399 (2011)CrossRefGoogle Scholar
  36. 36.
    Li, C., Sun, H., Liu, Z., Wang, M., Zheng, H., Wang, S.: Learning cross-modal deep representations for multi-modal MR image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 57–65. Springer, Cham (2019).
  37. 37.
    Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  38. 38.
    Margaritis, D.: Learning Bayesian network model structure from data. Ph.D. thesis, Carnegie-Mellon University Pittsburgh PA School Of Computer Science (2003)Google Scholar
  39. 39.
    Marques, J.P., et al.: Studying cyto and myeloarchitecture of the human cortex at ultra-high field with quantitative imaging: R1, R2(*) and magnetic susceptibility. Neuroimage 147, 152 (2017)CrossRefGoogle Scholar
  40. 40.
    Martino, D., et al.: The differential diagnosis of Huntington’s disease-like syndromes: ‘red flags’ for the clinician. J. Neurol. Neurosurg. Psychiatry 84, 650–656 (2013)CrossRefGoogle Scholar
  41. 41.
    Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659–667 (2014)CrossRefGoogle Scholar
  42. 42.
    Mellon, E.A., Beesam, R.S., Elliott, M.A., Reddy, R.: Mapping of cerebral oxidative metabolism with MRI. Proc. Nat. Acad. Sci. 107(26), 11787–11792 (2010)CrossRefGoogle Scholar
  43. 43.
    Montavon, G., Binder, A., Lapuschkin, S., Samek, W., Müller, K.-R.: Layer-wise relevance propagation: an overview. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 193–209. Springer, Cham (2019).
  44. 44.
    Mormina, E., et al.: Cerebellum and neurodegenerative diseases: beyond conventional magnetic resonance imaging. World J. Radiol. 9(10), 371–388 (2017). Scholar
  45. 45.
    Nasrallah, F.A.: Imaging brain deoxyglucose uptake and metabolism by glucoCEST MRI. J. Cereb. Blood Flow Metab. 33(8), 1270–1278 (2013)CrossRefGoogle Scholar
  46. 46.
    Ngen, E.J., Artemov, D.: Advances in monitoring cell-based therapies with magnetic resonance imaging: future perspectives. Int. J. Mol. Sci. 18, 1 (2017)CrossRefGoogle Scholar
  47. 47.
    Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-2011), pp. 689–696 (2011)Google Scholar
  48. 48.
    Nopoulos, P.C., et al.: Cerebral cortex structure in prodromal Huntington disease. Neurobiol. Dis. 40, 544–554 (2010).
  49. 49.
    Ofori, E., Du, G., Babcock, D., Huang, X., Vaillancourt, D.E.: Parkinson’s disease biomarkers program brain imaging repository. Neuroimage 124, 1120–1124 (2016). Scholar
  50. 50.
    O’Sullivan, S., Holzinger, A., Zatloukal, K., Saldiva, P., Sajid, M.I., Wichmann, D.: Machine learning enhanced virtual autopsy. Autops. Case Rep. 7(4), 3–7 (2017). Scholar
  51. 51.
    O’Sullivan, S., Holzinger, A., Wichmann, D., Saldiva, P., Sajid, M., Zatloukal, K.: Virtual autopsy: machine learning and artificial intelligence provide new opportunities for investigating minimal tumor burden and therapy resistance by cancer patients. Autops. Case Rep. 8, 1 (2018). Scholar
  52. 52.
    O’Sullivan, S., et al.: The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure. Brain Inform. 6(1), 3 (2019). Scholar
  53. 53.
    Pathak, D., Krahenbuhl, P., Darrell, T.: Constrained convolutional neural networks for weakly supervised segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1796–1804 (2015)Google Scholar
  54. 54.
    Poldrack, R.A., Gorgolewski, K.J.: Making big data open: data sharing in neuroimaging. Nature Neurosci. 17(11), 1510–1517 (2014)CrossRefGoogle Scholar
  55. 55.
    Rajalingam, B., Priya, R.: Multimodal medical image fusion based on deep learning neural network for clinical treatment analysis. Int. J. Chem. Tech. Res. CODEN (USA) IJCRGG 11, 0974–4290 (2018). ISSNGoogle Scholar
  56. 56.
    Rajpurkar, P., et al.: Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
  57. 57.
    Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process. Mag. 34(6), 96–108 (2017)CrossRefGoogle Scholar
  58. 58.
    Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. ACM (2016)Google Scholar
  59. 59.
    Sawiak, S.J., Morton, A.J.: The cambridge MRI database for animal models of Huntington disease. NeuroImage 124, 1260–1262 (2016)CrossRefGoogle Scholar
  60. 60.
    Sertbas, M., Ulgen, K.O.: Unlocking human brain metabolism by genome-scale and multiomics metabolic models: relevance for neurology research, health, and disease. OMICS 22(7), 455–467 (2018)CrossRefGoogle Scholar
  61. 61.
    Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35, 5 (2016)CrossRefGoogle Scholar
  62. 62.
    Smilkov, D., Thorat, N., Kim, B., Viégas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)
  63. 63.
    Szolovits, P., Patil, R.S., Schwartz, W.B.: Artificial intelligence in medical diagnosis. Ann. Intern. Med. 108, 1 (1988)CrossRefGoogle Scholar
  64. 64.
    Tabrizi, S.J., et al.: Biological and clinical manifestations of Huntington’s disease in the longitudinal TRACK-HD study: cross-sectional analysis of baseline data. Lancet Neurol. 8(9), 791–801 (2009). Scholar
  65. 65.
    Thompson, P.M., et al.: The enigma consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8(2), 153–182 (2014)CrossRefGoogle Scholar
  66. 66.
    Wang, J., et al.: Magnetic resonance imaging of glucose uptake and metabolism in patients with head and neck cancer. Sci. Rep. 6, 30618 (2016)CrossRefGoogle Scholar
  67. 67.
    Yaneske, E., Angione, C.: The poly-omics of ageing through individual-based metabolic modelling. BMC Bioinform. 19(14), 415 (2018)CrossRefGoogle Scholar
  68. 68.
    Yang, H., Rudin, C., Seltzer, M.: Scalable Bayesian rule lists. arXiv preprint arXiv:1602.08610 (2016)
  69. 69.
    Zampieri, G., Vijayakumar, S., Yaneske, E., Angione, C.: Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput. Biol. 15(7), e1007084 (2019)CrossRefGoogle Scholar
  70. 70.
    Zhang, X., et al.: PET/MR imaging: new frontier in Alzheimer’s disease and other dementias. Front. Mol. Neurosci. 10, 343 (2017)CrossRefGoogle Scholar
  71. 71.
    Zhou, T., Ruan, S., Canu, S.: A review: deep learning for medical image segmentation using multi-modality fusion. Array 3–4, 100004 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of PathologyFaculdade de Medicina Universidade de Sao PauloSão PauloBrazil
  2. 2.HCI-KDD, Holzinger Group, Institute for Medical Informatics and StatisticsMedical University of GrazGrazAustria
  3. 3.Department of Cognitive Linguistic and Psychological SciencesCarney Institute for Brain Science, Brown UniversityProvidenceUSA
  4. 4.Department of Computer Science and Information SystemsTeesside UniversityMiddlesbroughUK
  5. 5.Healthcare Innovation CentreTeesside UniversityMiddlesbroughUK

Personalised recommendations