ArticleBiomedical optics express2025
Lightweight CNN efficiently discriminates ovarian cancer cells from a tumor microenvironment via holographic imaging flow cytometry.
Article in Biomedical optics express, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Label-free detection of ovarian cancer cells in ascites-related cell models using digital holographic flow cytometry.Biomedical optics express · 2026Article
- Recent breakthroughs in digital holography, 2D/3D imaging, and holographic optical elements: introduction.Biomedical optics express · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Holographic imaging flow cytometry (HIFC) can generate 2D quantitative phase maps of flowing cells in microchannels. When combined with convolutional neural networks (CNNs), HIFC could provide a promising stain-free approach for identifying target cells in complex cellular environments by leveraging the distinctive morphological and optical properties of different cell types. Here, we propose a lightweight CNN for HIFC image classification, tailored to distinguish ovarian cancer cells from surrounding non-neoplastic cell populations of the tumor microenvironment (TME). We show that the proposed CNN outperforms commonly used models, i.e., Resnet and VGG, with a computational cost lower than Mobilenet, the benchmark for efficiency and accuracy. Our approach could streamline ovarian cancer diagnostics and improve understanding of the TME, ultimately aiding the development of personalized treatments.
Identifiers
What Socratic holds
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 Socratic graph.