# A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-primitive-study-on-unsupervised-anomaly-detection-with-an-autoencoder-in/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Daisuke Sato,Shouhei Hanaoka,Yukihiro Nomura,Tomomi Takenaga,Soichiro Miki,T. Yoshikawa,Naoto Hayashi,Osamu Abe |
| Citations | 56 |
| DOI | 10.1117/12.2292276 |
| Fields | Computer Science,Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2791423464 |
| Type | conference-paper |
| Year | 2018 |

## Paper authors

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

## Paper journal

- [Medical Imaging 2018: Computer-Aided Diagnosis](https://scholariq.org/journals/medical-imaging-2018-computer-aided-diagnosis/)

## 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/)
- [Medical Imaging Techniques and Applications](https://scholariq.org/topics/medical-imaging-techniques-and-applications/)

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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.