In American journal of clinical pathology ; h5-index 39.0
OBJECTIVES : Pathologic diagnosis of flat urothelial lesions is subject to high interobserver variability. We expected that deep learning could improve the accuracy and consistency of such pathologic diagnosis, although the learning process is a black box. We therefore propose a new approach for pathologic image classification incorporating the diagnostic process of the pathologist into a deep learning method.
METHODS : A total of 267 H&E-stained slides of normal urothelium and urothelial lesions from 127 cases were examined. Six independent convolutional neural networks were trained to classify pathologic images according to six pathologic criteria. We then used these networks in the main training for the final diagnosis.
RESULTS : Compared with conventional manual analysis, our method significantly improved the classification accuracy of images of flat urothelial lesions. The automated classification showed almost perfect agreement (weighted κ = 0.98) with the consensus reading. In addition, our approach provides the advantages of reliable diagnosis corresponding to histologic interpretation.
CONCLUSIONS : We used deep learning to establish an automated subtype classifier for flat urothelial lesions that successfully combines traditional morphologic approaches and complex deep learning to achieve a learning mechanism that seems plausible to the pathologist.
Nishikawa Toui, Iwamoto Ryuta, Matsuzaki Ibu, Musangile Fidele Yambayamba, Takahashi Ayata, Mikasa Yurina, Takahashi Yuichi, Kojima Fumiyoshi, Murata Shin-Ichi
Deep learning, Flat urothelial lesion, Genitourinary pathology, Pathologic criteria, Reliability