Delving into the Latest Updates on Renascience, Inc. with Synapse

作者: Saito-Koyama, Ryoko ; Uemura, Kodai ; Kawaguchi, Kurara ; Tajima, Yuichiro ; Suzuki, Takashi ; Kato, Sho ; Inoue, Chihiro ; Miyata, Toshio ; Sasaki, Tomoaki ; Takagi, Kiyoshi ; Yamazaki, Yuto ; Soma, Tomoya ; Yamaguchi, Mio

BackgroundA diagnosis with histological classification by pathologists is very important for appropriate treatments to improve the prognosis of patients with breast cancer. However, the number of pathologists is limited, and assisting the pathological diagnosis by artificial intelligence becomes very important. Here, we presented an automatic breast lesions detection model using microscopic histopathological images based on a Single Shot Multibox Detector (SSD) for the first time and evaluated its significance in assisting the diagnosis.MethodsWe built the data set and trained the SSD model with 1361 microscopic images and evaluated using 315 images. Pathologists and medical students diagnosed the images with or without the assistance of the model to investigate the significance of our model in assisting the diagnosis.ResultsThe model achieved 88.3% and 90.5% diagnostic accuracies in 3-class (benign, non-invasive carcinoma, or invasive carcinoma) or 2-class (benign or malignant) classification tasks, respectively, and the mean intersection over union was 0.59. Medical students achieved a remarkably higher diagnostic accuracy score (average 84.7%) with the assistance of the model compared to those without assistance (average 67.4%). Some people diagnosed images in a short time using the assistance of the model (shorten by average 6.4 min) while others required a longer time (extended by 7.2 min).ConclusionWe presented the automatic breast lesions detection method at high speed using histopathological micrographs. The present system may conveniently support the histological diagnosis by pathologists in laboratories.

100 项与 Renascience, Inc. 相关的药物交易

登录后查看更多信息

100 项与 Renascience, Inc. 相关的转化医学

登录后查看更多信息

组织架构

使用我们的机构树数据加速您的研究。

登录

或

查看完整样例数据

管线布局

2024年12月23日管线快照

管线布局中药物为当前组织机构及其子机构作为药物机构进行统计，早期临床1期并入临床1期，临床1/2期并入临床2期，临床2/3期并入临床3期

临床前

1

临床2期

临床3期

1

登录后查看更多信息

当前项目

药物(靶点)	适应症	全球最高研发状态

TM-5614 ( PAI-1 )	费城染色体阳性慢性粒细胞白血病更多	临床3期
盐酸吡多胺 ( Advanced glycation end product )	经前焦虑症更多	临床2期
Hereditary neurological intractable diseases(Luxna)	神经系统疾病更多	临床前