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International Conference on Bioengineering and Biomedical Signal and Image Processing

BIOMESIP 2021: Bioengineering and Biomedical Signal and Image Processing pp 179–193Cite as

Analysing Large Repositories of Medical Images

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12940))

Abstract

In clinical analysis, medical radiology is a widely used technique to make a noninvasive medical diagnosis that establishes the presence of an injury or disease without requiring invasive surgery. The purpose of computer-aided diagnosis (CAD) is to assist the clinician in interpreting the acquired data. In recent years, the application of machine learning techniques in this field has greatly increased, leading to increased accuracy or even complete replacement of manually created models. The main reason for the increased use of these techniques in medical image analysis is due to the fact that medical data has become increasingly available, the computational power of computers has increased, and significant advances have been made in machine learning, especially in machine vision applications. This development is a driving force behind major changes in the field of medicine, both in the laboratory and in the clinic. Unlike filtering techniques, machine learning can open up new methods for diagnosing diseases that were previously unthinkable. Moreover, the implementation of personalised medicine in the clinic, i.e. modelling specific conditions closely related to patient characteristics, requires the use of machine learning. In this paper, we give an overview of the field and present a set of guidelines that can be helpful in analysing large collections of medical images using data-driven techniques.

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Notes

  1. 1.

    https://www.dicomstandard.org/ (last accessed July 26, 2021).

References

  1. Abdel-Basset, M., Chang, V., Mohamed, R.: HSMA\(\_\)WOA: a hybrid novel slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Appl. Soft Comput. 95, 106642 (2020)

    Article  Google Scholar 

  2. Babenko, B., Yang, M.-H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011). https://doi.org/10.1109/TPAMI.2010.226

    Article  Google Scholar 

  3. Beasley, J.W., et al.: Information chaos in primary care: implications for physician performance and patient safety. J. Am. Board Fam. Med. 24(6), 745–751 (2011). http://www.jabfm.org/content/24/6/745.short

    Article  Google Scholar 

  4. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013). https://doi.org/10.1109/TPAMI.2013.50

    Article  Google Scholar 

  5. Berner, E.S. (ed.): Clinical Decision Support Systems: Theory and Practice, 3rd edn. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31913-1

    Book  Google Scholar 

  6. Bibault, J.E., Giraud, P., Burgun, A.: Big data and machine learning in radiation oncology: state of the art and future prospects. Cancer Lett. 382(1), 110–117 (2016). https://doi.org/10.1016/j.canlet.2016.05.033

    Article  Google Scholar 

  7. Bidgood, W.D., Horii, S.C., Prior, F.W., Van Syckle, D.E.: Understanding and using DICOM, the data interchange standard for biomedical imaging. J. Am. Med. Inform. Assoc. 4(3), 199–212 (1997). https://doi.org/10.1136/jamia.1997.0040199

    Article  Google Scholar 

  8. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017). https://doi.org/10.1162/tacl_a_00051

    Article  Google Scholar 

  9. Bozinovski, S.: Reminder of the first paper on transfer learning in neural networks, 1976. Informatica 44(3) (2020). https://doi.org/10.31449/INF.V44I3.2828

  10. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  11. de Bruijne, M.: Machine learning approaches in medical image analysis: from detection to diagnosis. Med. Image Anal. 33, 94–97 (2016). https://doi.org/10.1016/j.media.2016.06.032

    Article  Google Scholar 

  12. Burges, C.J.C., Schölkopf, B.: Improving the accuracy and speed of support vector machines. In: Advances in Neural Information Processing Systems 9, pp. 375–381 (1997). https://papers.nips.cc/paper/1253-improving-the-accuracy-and-speed-of-support-vector-machines

  13. Chen, S.-T., Cornelius, C., Martin, J., Chau, D.H.P.: ShapeShifter: robust physical adversarial attack on faster R-CNN object detector. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11051, pp. 52–68. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10925-7_4

    Chapter  Google Scholar 

  14. Chen, T., Carlos, G.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)

    Google Scholar 

  15. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Gated feedback recurrent neural networks. In: 32nd International Conference on Machine Learning, ICML 2015, vol. 3 (2015)

    Google Scholar 

  16. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL HLT 2019–2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, vol. 1 (2019)

    Google Scholar 

  17. Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4–5), 198–211 (2007). https://doi.org/10.1016/j.compmedimag.2007.02.002

    Article  Google Scholar 

  18. Gers, F.: Learning to forget: continual prediction with LSTM. In: 9th International Conference on Artificial Neural Networks: ICANN 1999, vol. 1999, pp. 850–855. IEEE (1999). https://doi.org/10.1049/cp:19991218

  19. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27, pp. 2672–2680 (2014)

    Google Scholar 

  20. Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649. IEEE (2013). https://doi.org/10.1109/ICASSP.2013.6638947

  21. Hamburg, M.A., Collins, F.S.: The path to personalized medicine. N. Engl. J. Med. 363(1), 1–3 (2010). https://doi.org/10.1056/NEJMp1002530

    Article  Google Scholar 

  22. Hinton, G.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. (JMLR) 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  23. Hinton, G.: Deep learning-a technology with the potential to transform health care. JAMA 320(11), 1101 (2018). https://doi.org/10.1001/jama.2018.11100

    Article  Google Scholar 

  24. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  25. Hržić, F., Štajduhar, I., Tschauner, S., Sorantin, E., Lerga, J.: Local-entropy based approach for X-ray image segmentation and fracture detection. Entropy 21(4), 338 (2019). https://doi.org/10.3390/E21040338

    Article  MathSciNet  Google Scholar 

  26. Hržić, F., Tschauner, S., Sorantin, E., Štajduhar, I.: XAOM: a method for automatic alignment and orientation of radiographs for computer-aided medical diagnosis. Comput. Biol. Med. 132, 104300 (2021). https://doi.org/10.1016/j.compbiomed.2021.104300

    Article  Google Scholar 

  27. Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cays visual cortex. J. Physiol. 160(1), 106–154 (1962)

    Article  Google Scholar 

  28. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, vol. 37 (2015)

    Google Scholar 

  29. Jain, V., Seung, H.S., Turaga, S.C.: Machines that learn to segment images: a crucial technology for connectomics. Curr. Opin. Neurobiol. 20(5), 653–666 (2010). https://doi.org/10.1016/j.conb.2010.07.004

    Article  Google Scholar 

  30. Kalinin, A.A., et al.: Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15(141), 20170387 (2018). https://doi.org/10.1098/rsif.2017.0387

    Article  Google Scholar 

  31. Kansagra, A.P., et al.: Big data and the future of radiology informatics. Acad. Radiol. 23(1), 30–42 (2016)

    Article  Google Scholar 

  32. Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., Wortman Vaughan, J.: Interpreting interpretability: understanding data scientists’ use of interpretability tools for machine learning. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020)

    Google Scholar 

  33. Khan, M.A., Sharif, M., Akram, T., Damaševičius, R., Maskeliūnas, R.: Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization. Diagnostics 11(5), 811 (2021)

    Article  Google Scholar 

  34. Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (2015)

    Google Scholar 

  35. Kiros, R., et al.: Skip-thought vectors. In: Advances in Neural Information Processing Systems 28 (NIPS 2015), pp. 3294–3302 (2015)

    Google Scholar 

  36. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)

    Google Scholar 

  37. Krsnik, I., Glavaš, G., Krsnik, M., Miletic, D., Štajduhar, I.: Automatic annotation of narrative radiology reports. Diagnostics 10(4), 196 (2020). https://doi.org/10.3390/diagnostics10040196

    Article  Google Scholar 

  38. Lal, S., et al.: Adversarial attack and defence through adversarial training and feature fusion for diabetic retinopathy recognition. Sensors 21(11), 3922 (2021)

    Article  Google Scholar 

  39. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  40. Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31–57 (2018)

    Article  Google Scholar 

  41. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017). https://doi.org/10.1016/J.MEDIA.2017.07.005

    Article  Google Scholar 

  42. Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE (2008). https://doi.org/10.1109/ICDM.2008.17. http://ieeexplore.ieee.org/document/4781136/

  43. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008). https://doi.org/10.1007/s10479-011-0841-3

  44. Manojlović, T., Ilić, D., Miletić, D., Štajduhar, I.: Using DICOM tags for clustering medical radiology images into visually similar groups. In: ICPRAM 2020 - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (2020). https://doi.org/10.5220/0008973405100517

  45. Manojlović, T., Milanič, M., Štajduhar, I.: Deep embedded clustering algorithm for clustering PACS repositories. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). IEEE (2021). https://doi.org/10.1109/CBMS52027.2021.00091

  46. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26 (NIPS 2013), pp. 3111–3119 (2013)

    Google Scholar 

  47. Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. Int. J. Comput. Vis. 79(3), 299–318 (2008). https://doi.org/10.1007/s11263-007-0122-4

    Article  Google Scholar 

  48. Obermeyer, Z., Emanuel, E.J.: Predicting the future - big data, machine learning, and clinical medicine. New Engl. J. Med. 375(13), 1216–1219 (2016). https://doi.org/10.1056/NEJMp1606181

    Article  Google Scholar 

  49. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  50. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Stroudsburg (2014). https://doi.org/10.3115/v1/D14-1162

  51. Poplin, R., et al.: Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2(3), 158–164 (2018). https://doi.org/10.1038/s41551-018-0195-0

    Article  Google Scholar 

  52. Radford, A., et al.: Learning transferable visual models from natural language supervision. arXiv preprint arXiv:2103.00020 (2021)

  53. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings (2016)

    Google Scholar 

  54. Rajkomar, A., et al.: Scalable and accurate deep learning for electronic health records. NPJ Digital Med. 1(Jan), 1–10 (2018). https://doi.org/10.1038/s41746-018-0029-1

  55. Ramasamy, L.K., Padinjappurathu, S.G., Kadry, S., Damaševičius, R.: Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier. PeerJ Comput. Sci. 7 (2021)

    Google Scholar 

  56. Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 512–519. IEEE Computer Society (2014)

    Google Scholar 

  57. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7

    Article  MATH  Google Scholar 

  58. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  59. Sahlol, A.T., Abd Elaziz, M., Tariq Jamal, A., Damaševičius, R., Farouk Hassan, O.: A novel method for detection of tuberculosis in chest radiographs using artificial ecosystem-based optimisation of deep neural network features. Symmetry 12(7), 1146 (2020)

    Article  Google Scholar 

  60. Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222–245 (2013). https://doi.org/10.1007/s11263-013-0636-x

    Article  MathSciNet  MATH  Google Scholar 

  61. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

  62. Štajduhar, I., Mamula, M., Miletić, D., Ünal, G.: Semi-automated detection of anterior cruciate ligament injury from MRI. Comput. Methods Programs Biomed. 140, 151–164 (2017). https://doi.org/10.1016/j.cmpb.2016.12.006

    Article  Google Scholar 

  63. Štajduhar, I., Tomić, M., Lerga, J.: Mirroring quasi-symmetric organ observations for reducing problem complexity. Expert Syst. Appl. 85, 318–334 (2017). https://doi.org/10.1016/j.eswa.2017.05.041

    Article  Google Scholar 

  64. Vincent, G., Wolstenholme, C., Scott, I., Bowes, M.: Fully automatic segmentation of the knee joint using active appearance models. In: Medical Image Analysis for the Clinic: A Grand Challenge, pp. 224–230. CreateSpace (2010)

    Google Scholar 

  65. Wagholikar, K.B., Sundararajan, V., Deshpande, A.W.: Modeling paradigms for medical diagnostic decision support: a survey and future directions. J. Med. Syst. 36(5), 3029–3049 (2012)

    Article  Google Scholar 

  66. Wang, S., Summers, R.M.: Machine learning and radiology. Med. Image Anal. 16(5), 933–951 (2012). https://doi.org/10.1016/j.media.2012.02.005

    Article  Google Scholar 

  67. Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: 33rd International Conference on Machine Learning, ICML 2016, vol. 1 (2016)

    Google Scholar 

  68. Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the 32nd International Conference on Machine Learning, vol. 37, pp. 2048–2057 (2015). https://doi.org/10.1016/j.scitotenv.2016.07.196

  69. Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)

    Google Scholar 

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Acknowledgement

This work has been supported in part by Croatian Science Foundation [grant number IP-2020-02-3770]; and by the University of Rijeka, Croatia [grant number uniri-tehnic-18-15].

Author information

Authors and Affiliations

  1. Faculty of Engineering, University of Rijeka, Rijeka, Croatia

    Ivan Štajduhar, Teo Manojlović, Franko Hržić & Mateja Napravnik

  2. Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Rijeka, Croatia

    Ivan Štajduhar, Teo Manojlović & Franko Hržić

  3. School of Business Informatics and Mathematics, University of Mannheim, Mannheim, Germany

    Goran Glavaš

  4. Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia

    Matija Milanič

  5. Jozef Stefan Institute, Ljubljana, Slovenia

    Matija Milanič

  6. Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria

    Sebastian Tschauner

  7. Clinical Hospital Centre Rijeka, Rijeka, Croatia

    Mihaela Mamula Saračević & Damir Miletić

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  1. Ivan Štajduhar
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Correspondence to Ivan Štajduhar .

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  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Daniel Castillo-Secilla

  3. University of Granada, Granada, Spain

    Luis Javier Herrera

  4. Universidad de Granada, Granada, Granada, Spain

    Héctor Pomares

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Štajduhar, I. et al. (2021). Analysing Large Repositories of Medical Images. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_17

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