Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods
| Autor: | Hessel Wijkstra, Massimo Mischi, R.R. Wildeboer, Ruud J. G. van Sloun |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
medicine.medical_specialty Artificial intelligence Computer science Health Informatics 030218 nuclear medicine & medical imaging 03 medical and health sciences Prostate cancer 0302 clinical medicine Deep Learning Magnetic resonance imaging SDG 3 - Good Health and Well-being Machine learning Ultrasound medicine Humans Medical physics Diagnosis Computer-Assisted Computer-aided detection medicine.diagnostic_test business.industry Deep learning Multiparametric imaging Prostatic Neoplasms Computer-aided diagnosis medicine.disease Computer aided detection 3. Good health Computer Science Applications Focus (optics) business 030217 neurology & neurosurgery Software |
| Zdroj: | Computer Methods and Programs in Biomedicine. 189 |
| ISSN: | 0169-2607 |
| DOI: | 10.1016/j.cmpb.2020.105316 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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