Distribution of average recognition error rates on 96 images with equidispersion when repeatedly implementing the hyperparameter-tuning method using n unlabeled images 1000 times.

In each replicate, a few (3, 5, 10, 20, 30, 40, or 50) images with the label erased are randomly selected from 96 images, and λ is tuned so that the mean of the counting equals the variance. Then, the average recognition error rate is calculated on the 96 labeled images u...

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Bibliographic Details
Main Author:	Sijie Chen (1622476) (author)
Other Authors:	Po-Hsun Huang (183737) (author), Hyungseok Kim (14635448) (author), Yuhe Cui (20902959) (author), Cullen R. Buie (471587) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Molecular Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified escherichia coli </ average error rate assessing microbial growth requires two hyperparameters regional candidate circles propose statistical methods efficient colony counting limited labeled data div >< p 54 %), autocellseg accurate colony counting colony counting 54 %), regional algorithms hyperparameters using propose mcount throughput workflows throughput plating promising solution precisely labeled merged colonies known solution facilitate deployment contour information common occurrence accurately infer 960 images 847 segments 31 %).
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Distribution of average recognition error rates on 96 images with equidispersion when repeatedly implementing the hyperparameter-tuning method using <i>n</i> unlabeled images 1000 times.

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