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
Autores principales: Nakao, Takahiro, Hanaoka, Shouhei, Nomura, Yukihiro, Murata, Masaki, Takenaga, Tomomi, Miki, Soichiro, Watadani, Takeyuki, Yoshikawa, Takeharu, Hayashi, Naoto, Abe, Osamu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8289984/
https://www.ncbi.nlm.nih.gov/pubmed/33555397
http://dx.doi.org/10.1007/s10278-020-00413-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8289984/
https://www.ncbi.nlm.nih.gov/pubmed/33555397
http://dx.doi.org/10.1007/s10278-020-00413-2

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