# Unsupervised Deep Anomaly Detection in Chest Radiographs

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/unsupervised-deep-anomaly-detection-in-chest-radiographs/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Takahiro Nakao,Shouhei Hanaoka,Yukihiro Nomura,Masaki Murata,Tomomi Takenaga,Soichiro Miki,Takeyuki Watadani,T. Yoshikawa,Naoto Hayashi,Osamu Abe |
| Citations | 90 |
| DOI | 10.1007/s10278-020-00413-2 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://link.springer.com/content/pdf/10.1007/s10278-020-00413-2.pdf |
| OpenAlex ID | https://openalex.org/W3127694486 |
| PMID | 33555397 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Yukihiro Nomura](https://scholariq.org/researchers/yukihiro-nomura/)

## Paper primary topic

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)

## Paper topics

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)
- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.