Distribution of average recognition error rates on the benchmark dataset when repeatedly implementing the hyperparameter-tuning method using n labeled images 1000 times.

In each replicate, a few (1, 3, 5, 10, 20, 30, or 50) images are randomly selected from the benchmark dataset, and λ is tuned for each image to a proper value so that MCount gives the same counting number as the label. Then, the average of λ is chosen f...

Full description

Saved in:

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 %).
Tags:	Add Tag No Tags, Be the first to tag this record!

Distribution of average recognition error rates on the benchmark dataset when repeatedly implementing the hyperparameter-tuning method using <i>n</i> labeled images 1000 times.

Similar Items