Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Research output: Contribution to journalReview articleAcademicpeer-review

Abstract

Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.

Original languageEnglish
Article number105316
Number of pages15
JournalComputer Methods and Programs in Biomedicine
Volume189
DOIs
Publication statusPublished - Jun 2020

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Artificial Intelligence
Artificial intelligence
Prostatic Neoplasms
Learning
Imaging techniques
Pathology
Weighing
Decision making
Deep learning

Keywords

  • Artificial intelligence
  • Computer-aided detection
  • Computer-aided diagnosis
  • Machine learning
  • Magnetic resonance imaging
  • Multiparametric imaging
  • Prostate cancer
  • Ultrasound

Cite this

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title = "Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods",
abstract = "Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.",
keywords = "Artificial intelligence, Computer-aided detection, Computer-aided diagnosis, Machine learning, Magnetic resonance imaging, Multiparametric imaging, Prostate cancer, Ultrasound",
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