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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744870 | PMC |
| http://dx.doi.org/10.1038/s41467-022-35109-4 | DOI Listing |
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Target Before Shooting: Accurate Anomaly Detection and Localization Under One Millisecond via Cascade Patch Retrieval.
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In this work, by re-examining the "matching" nature of Anomaly Detection (AD), we propose a novel AD framework that simultaneously enjoys new records of AD accuracy and dramatically high running speed. In this framework, the anomaly detection problem is solved via a cascade patch retrieval procedure that retrieves the nearest neighbors for each test image patch in a coarse-to-fine fashion. Given a test sample, the top-K most similar training images are first selected based on a robust histogram matching process.
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The pace and trajectory of global and local environmental changes are jeopardizing our health in numerous ways, among them exacerbating the risk of disease emergence and spread in both the community and the healthcare setting via healthcare-associated infections (HAIs). Factors such as climate change, widespread land alteration, and biodiversity loss underlie changing human-animal-environment interactions that drive disease vectors, pathogen spillover, and cross-species transmission of zoonoses. Climate change-associated extreme weather events also threaten critical healthcare infrastructure, infection prevention and control (IPC) efforts, and treatment continuity, adding to stress to strained systems and creating new areas of vulnerability.
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