[PB-1～PB-52] ポスターB

[PB-5] Deep learning based presuming for molecular biological profiles of endometrial cancer using digital images of HE-stained specimens

*MINA UMEMOTO¹, TASUKU MARIYA¹, MAI NAGATA², TOSHIHIRO HORIMAI³, SHOTA SHINKAI¹, SHINTARO SUGITA⁴, TAKAYUKI KANASEKI⁵, MOTOKI MATSUURA¹, MASAHIRO IWASAKI¹, YOSHIHIKO HIROHASHI⁵, TADASHI HASEGAWA⁴, TOSHIHIKO TORIGOE⁵, TSUYOSHI SAITO¹, YUICHI FUJINO² (1. Department of Obstetrics and Gynecology, Sapporo Medical University School, 2. Department of Media Architecture, Future University Hakodate, Hokkaido, Japan, 3. Limited Liability Company Gomes Company, Sapporo, Hokkaido, Japan, 4. Department of Surgical Pathology, Sapporo Medical University, Sapporo, Hokkaido, Japan, 5. Department of Pathology, Sapporo Medical University School, Hokkaido, Japan)