ArticleNPJ biofilms and microbiomes2025
Deep generative modeling of annotated bacterial biofilm images.
Article in NPJ biofilms and microbiomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Intelligent Plasmonic Bio-Holoelectrochemical Systems: A Testable Architecture for Light-Programmed Microbial Electrochemistry.Global challenges (Hoboken, NJ) · 2026Review
- Deep learning-based high-information-content graph representation of early stage bacterial biofilms.NPJ biofilms and microbiomes · 2026Article
- BacQuant: A Scalable Automated Image Processing Pipeline for Quantifying Biofilm Aggregates.microPublication biology · 2026Article
- Artificial intelligence applications in biofilm research: computational approaches for biofilm analysis and antimicrobial prediction.Frontiers in microbiology · 2026Review
- Biofilms Exposed: Innovative Imaging and Therapeutic Platforms for Persistent Infections.Antibiotics (Basel, Switzerland) · 2025Review
- ViTKAB: an efficient deep learning network for cotton leaf disease identification.Frontiers in plant science · 2025Article
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Authors and funding
7 authors.
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Abstract
Biofilms are critical for understanding environmental processes, developing biotechnology applications, and progressing in medical treatments of various infections. Nowadays, a key limiting factor for biofilm analysis is the difficulty in obtaining large datasets with fully annotated images. This study introduces a versatile approach for creating synthetic datasets of annotated biofilm images with employing deep generative modeling techniques, including VAEs, GANs, diffusion models, and CycleGAN. Synthetic datasets can significantly improve the training of computer vision models for automated biofilm analysis, as demonstrated with the application of Mask R-CNN detection model. The approach represents a key advance in the field of biofilm research, offering a scalable solution for generating high-quality training data and working with different strains of microorganisms at different stages of formation. Terabyte-scale datasets can be easily generated on personal computers. A web application is provided for the on-demand generation of biofilm images.
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Registered trials
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