卷积神经网络
医学
学习迁移
人工智能
膀胱镜检查
深度学习
膀胱癌
人工神经网络
接收机工作特性
放射科
机器学习
癌症
病理
计算机科学
内科学
替代医学
作者
Atsushi Ikeda,Hirokazu Nosato,Yuta Kochi,Hiromitsu Negoro,Takahiro Kojima,Hidenori Sakanashi,Masahiro Murakawa,Hiroyuki Nishiyama
出处
期刊:Journal of Endourology
[Mary Ann Liebert]
日期:2021-07-01
卷期号:35 (7): 1030-1035
被引量:17
标识
DOI:10.1089/end.2020.0919
摘要
Background: Nonmuscle-invasive bladder cancer is diagnosed, treated, and monitored using cystoscopy. Artificial intelligence (AI) is increasingly used to augment tumor detection, but its performance is hindered by the limited availability of cystoscopic images required to form a large training data set. This study aimed to determine whether stepwise transfer learning with general images followed by gastroscopic images can improve the accuracy of bladder tumor detection on cystoscopic imaging. Materials and Methods: We trained a convolutional neural network with 1.2 million general images, followed by 8728 gastroscopic images. In the final step of the transfer learning process, the model was additionally trained with 2102 cystoscopic images of normal bladder tissue and bladder tumors collected at the University of Tsukuba Hospital. The diagnostic accuracy was evaluated using a receiver operating characteristic curve. The diagnostic performance of the models trained with cystoscopic images with or without stepwise organic transfer learning was compared with that of medical students and urologists with varying levels of experience. Results: The model developed by stepwise organic transfer learning had 95.4% sensitivity and 97.6% specificity. This performance was better than that of the other models and comparable with that of expert urologists. Notably, it showed superior diagnostic accuracy when tumors occupied >10% of the image. Conclusions: Our findings demonstrate the value of stepwise organic transfer learning in applications with limited data sets for training and further confirm the value of AI in medical diagnostics. Here, we applied deep learning to develop a tool to detect bladder tumors with an accuracy comparable with that of a urologist. To address the limitation that few bladder tumor images are available to train the model, we demonstrate that pretraining with general and gastroscopic images yields superior results.
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