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...

Descripció completa

Guardat en:
Dades bibliogràfiques
Publicat a:J Digit Imaging
Autors principals: Nakao, Takahiro, Hanaoka, Shouhei, Nomura, Yukihiro, Murata, Masaki, Takenaga, Tomomi, Miki, Soichiro, Watadani, Takeyuki, Yoshikawa, Takeharu, Hayashi, Naoto, Abe, Osamu
Format: Artigo
Idioma:Inglês
Publicat: Springer International Publishing 2021
Matèries:
Original Paper
Accés en línia:https://ncbi.nlm.nih.gov/pmc/articles/PMC8289984/
https://ncbi.nlm.nih.gov/pubmed/33555397
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s10278-020-00413-2
Etiquetes: Afegir etiqueta
Sense etiquetes, Sigues el primer a etiquetar aquest registre!

Ítems similars