Unsupervised Deep Anomaly Detection in Chest Radiographs.

The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial netwo...

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Detalles Bibliográficos

Publicado en:	Journal of Digital Imaging Vol. 34; no. 2; pp. 418 - 428
Autores principales:	Nakao, Takahiro, Hanaoka, Shouhei, Nomura, Yukihiro, Murata, Masaki, Takenaga, Tomomi, Miki, Soichiro, Watadani, Takeyuki, Yoshikawa, Takeharu, Hayashi, Naoto, Abe, Osamu
Formato:	diagnostic images research tables/charts Journal Article
Publicado:	Springer Nature Apr2021
Acceso en línea:	Ver este registro en EBSCOhost