| DC Field | Value | Language |
|---|
| dc.contributor.author | Kosik, I. | - |
| dc.contributor.author | Nedzved, A. | - |
| dc.contributor.author | Karapetsian, R. | - |
| dc.contributor.author | Yashina, V. | - |
| dc.contributor.author | Gurevich, I. | - |
| dc.date.accessioned | 2021-11-05T10:39:40Z | - |
| dc.date.available | 2021-11-05T10:39:40Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.citation | Automation of the Study of Radiologically Isolated Syndrome in Multiple Sclerosis / Kosik I. [et al.] // Pattern Recognition and Information Processing (PRIP'2021) = Распознавание образов и обработка информации (2021) : Proceedings of the 15th International Conference, 21–24 Sept. 2021, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2021. – P. 187–190. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/45825 | - |
| dc.description.abstract | In this paper the UNet 3+ model is used for detection regions of multiple sclerosis on radiological images. For increase quality the specific image preprocessing improves quality of dataset and results of detection. The proposed solution for the automatic identification of pathological areas using artificial neural networks has significantly increased the speed of analyzing the state of the pathological pattern. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | UIIP NASB | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | conference proceedings | ru_RU |
| dc.subject | medical image analysis | ru_RU |
| dc.subject | UNet 3+ | ru_RU |
| dc.subject | regions detection | ru_RU |
| dc.subject | segmentation | ru_RU |
| dc.subject | dataset preprocessing | ru_RU |
| dc.title | Automation of the Study of Radiologically Isolated Syndrome in Multiple Sclerosis | ru_RU |
| dc.type | Статья | ru_RU |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2021) = Распознавание образов и обработка информации (2021)
|
