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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 Lecture Notes in Computer Science book series (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.

Keywords

  • Information fusion
  • Clinical medicine
  • Big data
  • Machine learning
  • Deep learning
  • Transfer learning
  • Image analysis
  • DICOM
  • Natural language processing

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Fig. 1.
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Notes

  1. 1.

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

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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].

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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

    Prof. 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

    Prof. 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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