A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography
- DOI
- 10.3390/s23052864
- Published
- 2023-03-06
- Container
- Sensors
- Publisher
- MDPI AG
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.3390/s23052864,
title = {A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography},
author = {Changhee Yun and Bomi Eom and Sungjun Park and Chanho Kim and Dohwan Kim and Farah Jabeen and Won Hwa Kim and Hye Jung Kim and Jaeil Kim},
year = {2023},
journal = {Sensors},
doi = {10.3390/s23052864},
url = {https://doi.org/10.3390/s23052864}
}RIS
TY - JOUR TI - A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography AU - Changhee Yun AU - Bomi Eom AU - Sungjun Park AU - Chanho Kim AU - Dohwan Kim AU - Farah Jabeen AU - Won Hwa Kim AU - Hye Jung Kim AU - Jaeil Kim PY - 2023 JO - Sensors DO - 10.3390/s23052864 UR - https://doi.org/10.3390/s23052864 ER -
APA
Yun, C., Eom, B., Park, S., Kim, C., Kim, D., Jabeen, F., Kim, W. H., Kim, H. J., & Kim, J. (2023). A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography. Sensors. https://doi.org/10.3390/s23052864
Source records
- crossref · retrieved 2026-09-25T09:19:33.154Z