ArticleBiomedical optics express2024
Adversarial training collaborating hybrid convolution-transformer network for automatic identification of reactive lymphocytes in peripheral blood.
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 1 paper.
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
1 citing paper in PubMed.
- Adversarial training collaborating hybrid convolution-transformer network for automatic identification of reactive lymphocytes in peripheral blood.Biomedical optics express · 2024Article
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
Reactive lymphocytes may indicate diseases such as viral infections. Identifying these abnormal lymphocytes is crucial for disease diagnosis. Currently, reactive lymphocytes are mainly manually identified by pathological experts with microscopes and morphological knowledge, which is time-consuming and laborious. Some studies have used convolutional neural networks (CNNs) to identify peripheral blood leukocytes, but there are limitations in the small receptive field of the model. Our model introduces a transformer based on CNN, expands the receptive field of the model, and enables it to extract global features more efficiently. We also enhance the generalization ability of the model through virtual adversarial training (VAT) without changing the parameters of the model. Finally, our model achieves an overall accuracy of 93.66% on the test set, and the accuracy of reactive lymphocytes also reaches 88.03%. This work takes another step toward the efficient identification of reactive lymphocytes.
Identifiers
What OpenQuestion 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 OpenQuestion graph.