Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder.
Anomaly detection has been applied in the various disease of medical practice, such as breast cancer, retinal, lung lesion, and skin disease. However, in real-world anomaly detection, there exist a large number of healthy samples, and but very few sick samples. To alleviate the problem of data imbal...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 2; pp. 153 - 162 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2022
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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=155757673&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155757673 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2022 vid: 35 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155757673 154588071 155757673 155757673 10.1007/s10278-021-00558-8 155757673 ppf: 153 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Unsupervised Deep Anomaly Detection for Medical Images Using an Improved Adversarial Autoencoder. aug: au: Zhang, Haibo Guo, Wenping Zhang, Shiqing Lu, Hongsheng Zhao, Xiaoming affil: Taizhou Central Hospital (Taizhou University Hospital), Taizhou University, 318000, Zhejiang, China sug: subj: Diagnostic Imaging Image Enhancement Radiography Methods Abnormalities Diagnosis Autoencoder Semantics Minimum Data Set ab: Anomaly detection has been applied in the various disease of medical practice, such as breast cancer, retinal, lung lesion, and skin disease. However, in real-world anomaly detection, there exist a large number of healthy samples, and but very few sick samples. To alleviate the problem of data imbalance in anomaly detection, this paper proposes an unsupervised learning method for deep anomaly detection based on an improved adversarial autoencoder, in which a module called chain of convolutional block (CCB) is employed instead of the conventional skip-connections used in adversarial autoencoder. Such CCB connections provide considerable advantages via direct connections, not only preserving both global and local information but also alleviating the problem of semantic disparity between the encoding features and the corresponding decoding features. The proposed method is thus able to capture the distribution of normal samples within both image space and latent vector space. By means of minimizing the reconstruction error within both spaces during training phase, higher reconstruction error during test phase is indicative of an anomaly. Our method is trained only on the healthy persons in order to learn the distribution of normal samples and can detect sick samples based on high deviation from the distribution of normality in an unsupervised way. Experimental results for multiple datasets from different fields demonstrate that the proposed method yields superior performance to state-of-the-art methods. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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