Articolul precedent |
Articolul urmator |
296 1 |
Ultima descărcare din IBN: 2023-11-10 03:45 |
SM ISO690:2012 CHATZAKIS, D., DERMITZAKIS, Aris, PALLIKARAKIS, Nicolas. Deep Learning Methods for Tumor Segmentation and Detection in X-Ray Breast Imaging. In: Nanotechnologies and Biomedical Engineering, Ed. 5, 3-5 noiembrie 2021, Chişinău. Chişinău: Pontos, 2021, Ediția 5, p. 124. ISBN 978-9975-72-592-7. |
EXPORT metadate: Google Scholar Crossref CERIF DataCite Dublin Core |
Nanotechnologies and Biomedical Engineering Ediția 5, 2021 |
||||||
Conferința "Nanotechnologies and Biomedical Engineering" 5, Chişinău, Moldova, 3-5 noiembrie 2021 | ||||||
|
||||||
Pag. 124-124 | ||||||
|
||||||
Descarcă PDF | ||||||
Rezumat | ||||||
Recently there have been a series of machine learning methods or deep learning architectures that have been developed and used in the field medical imaging. In this study, we focus on the use of AI in the field of breast imaging and the methods with the highest accuracy results for breast tumor segmentation and classification are presented, achieving robust results in detection. Extensive research which included more than 150 related published papers was performed, containing results published between 2016 to 2020 resulting in a review of 4 selected models all at the forefront of current progress. |
||||||
|