ArticleBiomedical optics express2024
Deep learning-assisted low-cost autofluorescence microscopy for rapid slide-free imaging with virtual histological staining.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence assisted multi-model pathological diagnosis of breast cancer based on multispectral autofluorescence images.NPJ breast cancer · 2026Article
- Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.Frontiers in artificial intelligence · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Slide-free imaging techniques have shown great promise in improving the histological workflow. For example, computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP) has achieved high resolution with a long depth of field, which, however, requires a costly ultraviolet laser. Here, simply using a low-cost light-emitting diode (LED), we propose a deep learning-assisted framework of enhanced widefield microscopy, termed EW-LED, to generate results similar to CHAMP (the learning target). Comparing EW-LED and CHAMP, EW-LED reduces the cost by 85×, shortening the image acquisition time and computation time by 36× and 17×, respectively. This framework can be applied to other imaging modalities, enhancing widefield images for better virtual histology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.