dc.contributor.author | CHATZAKIS, D. | |
dc.contributor.author | DERMITZAKIS, A. | |
dc.contributor.author | PALLIKARAKIS, N. | |
dc.date.accessioned | 2021-11-15T14:06:07Z | |
dc.date.available | 2021-11-15T14:06:07Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | CHATZAKIS, D., DERMITZAKIS, A., PALLIKARAKIS, N. Deep Learning Methods for Tumor Segmentation and Detection in X-Ray Breast Imaging. In: ICNMBE-2021: the 5th International Conference on Nanotechnologies and Biomedical Engineering, November 3-5, 2021: Program and abstract book. Chişinău, 2021, p. 124. ISBN 978-9975-72-592-7. | en_US |
dc.identifier.isbn | 978-9975-72-592-7 | |
dc.identifier.uri | http://repository.utm.md/handle/5014/18067 | |
dc.description | Only Abstract. | en_US |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Universitatea Tehnică a Moldovei | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | tumor segmentation | en_US |
dc.subject | tumor detection | en_US |
dc.subject | X-Ray breast imaging | en_US |
dc.title | Deep Learning Methods for Tumor Segmentation and Detection in X-Ray Breast Imaging | en_US |
dc.type | Article | en_US |
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