| DC Field | Value | Language |
|---|
| dc.contributor.author | Zhao Di | - |
| dc.contributor.author | Tang Yi | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2024-08-16T06:49:22Z | - |
| dc.date.available | 2024-08-16T06:49:22Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.citation | Zhao Di. Generative adversarial network for medical image segmentation / Zhao Di, Tang Yi // Информационные технологии и управление : материалы 60-ой научной конференции аспирантов, магистрантов и студентов, Минск, 22–26 апреля 2024 года / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: Л. Ю. Шилин [и др.]. – Минск, 2024. – С. 44. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/56938 | - |
| dc.description.abstract | In this paper, a U-Net medical image segmentation method based on generative adversarial networks is proposed. This method is used to solve the problem of performance degradation of modeling algorithms due to insufficient training samples in medical image segmentation. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | БГУИР | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | segmentation method | en_US |
| dc.subject | medical image | en_US |
| dc.subject | medical image segmentation | en_US |
| dc.title | Generative adversarial network for medical image segmentation | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Информационные технологии и управление : материалы 60-й научной конференции аспирантов, магистрантов и студентов (2024)
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