イシハマ　タカノリ   Takanori Ishihama   石濱　嵩統    所属   川崎医科大学  医学部 臨床医学　歯科総合口腔医療学    職種   臨床助教８年
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Training high-performance deep learning classifier for diagnosis in oral cytology using diverse annotations.
掲載誌名	正式名：Scientific reports 略　 称：Sci Rep ISSNコード：20452322/20452322
掲載区分	国外
巻・号・頁	14(1),pp.17591
著者・共著者	Shintaro Sukegawa, Futa Tanaka, Keisuke Nakano, Takeshi Hara, Takanaga Ochiai, Katsumitsu Shimada, Yuta Inoue, Yoshihiro Taki, Fumi Nakai, Yasuhiro Nakai, Takanori Ishihama, Ryo Miyazaki, Satoshi Murakami, Hitoshi Nagatsuka, Minoru Miyake
発行年月	2024/07
概要	The uncertainty of true labels in medical images hinders diagnosis owing to the variability across professionals when applying deep learning models. We used deep learning to obtain an optimal convolutional neural network (CNN) by adequately annotating data for oral exfoliative cytology considering labels from multiple oral pathologists. Six whole-slide images were processed using QuPath for segmenting them into tiles. The images were labeled by three oral pathologists, resulting in 14,535 images with the corresponding pathologists' annotations. Data from three pathologists who provided the same diagnosis were labeled as ground truth (GT) and used for testing. We investigated six models trained using the annotations of (1) pathologist A, (2) pathologist B, (3) pathologist C, (4) GT, (5) majority voting, and (6) a probabilistic model. We divided the test by cross-validation per slide dataset and examined the classification performance of the CNN with a ResNet50 baseline. Statistical evaluation was performed repeatedly and independently using every slide 10 times as test data. For the area under the curve, three cases showed the highest values (0.861, 0.955, and 0.991) for the probabilistic model. Regarding accuracy, two cases showed the highest values (0.988 and 0.967). For the models using the pathologists and GT annotations, many slides showed very low accuracy and large variations across tests. Hence, the classifier trained with probabilistic labels provided the optimal CNN for oral exfoliative cytology considering diagnoses from multiple pathologists. These results may lead to trusted medical artificial intelligence solutions that reflect diverse diagnoses of various professionals.
DOI	10.1038/s41598-024-67879-w
PMID	39080384