Naslov
Analysing Large Repositories of Medical Images

Autori
Štajduhar, Ivan ; Manojlović, Teo ; Hržić, Franko ; Napravnik, Mateja ; Glavaš, Goran ; Milanič, Matija ; Tschauner, Sebastian ; Mamula Saračević, Mihaela ; Miletić, Damir

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Bioengineering and Biomedical Signal and Image Processing / - , 2021, 179-193

ISBN
978-3-030-88163-4

Skup
International Conference on Bioengineering, Biomedical Signal and Image Processing (BIOMESIP 2021)

Mjesto i datum
Las Palmas de Gran Canaria, Španjolska, 19.07.2021. - 21.07.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
information fusion ; clinical medicine ; big data ; machine learning ; deep learning ; transfer learning ; image analysis ; DICOM ; natural language processing

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Kliničke medicinske znanosti

POVEZANOST RADA