# HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/healthygan-learning-from-unannotated-medical-images-to-detect-anomalies/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Md Mahfuzur Rahman Siddiquee,Jay Shah,Teresa Wu,Catherine D. Chong,Todd J. Schwedt,Baoxin Li |
| Citations | 14 |
| DOI | 10.1007/978-3-031-16980-9_5 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | green |
| OA URL | https://www.ncbi.nlm.nih.gov/pmc/articles/11062325 |
| OpenAlex ID | https://openalex.org/W4296960558 |
| PMID | 38694707 |
| Type | conference-paper |
| Year | 2022 |

## Paper authors

- [Baoxin Li](https://scholariq.org/researchers/baoxin-li-2/)

## Paper journal

- [Lecture notes in computer science](https://scholariq.org/journals/lecture-notes-in-computer-science/)

## 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/)
- [Generative Adversarial Networks and Image Synthesis](https://scholariq.org/topics/generative-adversarial-networks-and-image-synthesis/)

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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.