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...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 2; pp. 418 - 428 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151472667&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151472667 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2021 vid: 34 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151472667 148574070 151472667 151472667 10.1007/s10278-020-00413-2 151472667 ppf: 418 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Unsupervised Deep Anomaly Detection in Chest Radiographs. aug: au: Nakao, Takahiro Hanaoka, Shouhei Nomura, Yukihiro Murata, Masaki Takenaga, Tomomi Miki, Soichiro Watadani, Takeyuki Yoshikawa, Takeharu Hayashi, Naoto Abe, Osamu affil: Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan sug: subj: Radiography, Thoracic Methods Human Male Female Middle Age Random Sample Descriptive Statistics ROC Curve Middle Aged: 45-64 years Male Female ab: 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 network (α-GAN) framework, which is a combination of a GAN and a variational autoencoder, as a DNN model. A total of 29,684 frontal chest radiographs from the Radiological Society of North America Pneumonia Detection Challenge dataset were used for this study (16,880 male and 12,804 female patients; average age, 47.0 years). All these images were labeled as "Normal," "No Opacity/Not Normal," or "Opacity" by board-certified radiologists. About 70% (6,853/9,790) of the Normal images were randomly sampled as the training dataset, and the rest were randomly split into the validation and test datasets in a ratio of 1:2 (7,610 and 15,221). Our anomaly detection system could correctly visualize various lesions including a lung mass, cardiomegaly, pleural effusion, bilateral hilar lymphadenopathy, and even dextrocardia. Our system detected the abnormal images with an area under the receiver operating characteristic curve (AUROC) of 0.752. The AUROCs for the abnormal labels Opacity and No Opacity/Not Normal were 0.838 and 0.704, respectively. Our DNN-based unsupervised anomaly detection method could successfully detect various diseases or anomalies in chest radiographs by training with only the normal images. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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