ArticleScientific reports2022
Self-normalized density map (SNDM) for counting microbiological objects.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Deep neural networks in medical microbiology for bacterial colonies classification.Scientific reports · 2026Article
- Detecting mangrove seedlings from UAV imagery using deep learning for restoration monitoring.Scientific reports · 2025Article
- UMicroorganisms · 2024Article
- Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation.Sensors (Basel, Switzerland) · 2023Article
- Deep learning for diffusion in porous media.Scientific reports · 2023Article
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Authors and funding
4 authors.
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Abstract
The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U[Formula: see text]-Net. Two statistical methods for deep neural networks are utilized: the bootstrap and the Monte Carlo (MC) dropout. The detailed analysis of the uncertainties for the DM predictions leads to a deeper understanding of the DM model's deficiencies. Based on our investigation, we propose a self-normalization module in the network. The improved network model, called Self-Normalized Density Map (SNDM), can correct its output density map by itself to accurately predict the total number of objects in the image. The SNDM architecture outperforms the original model. Moreover, both statistical frameworks-bootstrap and MC dropout-have consistent statistical results for SNDM, which were not observed in the original model. The SNDM efficiency is comparable with the detector-base models, such as Faster and Cascade R-CNN detectors.
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