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Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking
Anomaly detection and segmentation pose an important task across sectors ranging from medical imaging analysis to industry quality control. However, current unsupervised approaches require training data to not contain any anomalies, a requirement that can be especially challenging in many medical im...
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| Pubblicato in: | IEEE Int Conf Comput Vis Workshops |
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| Autori principali: | , , , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7616405/ https://ncbi.nlm.nih.gov/pubmed/39205863 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/ICCVW60793.2023.00254 |
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