ArticleQuantitative imaging in medicine and surgery2025
Fine-grained pathomorphology recognition of cervical lesions with a dropped multibranch Swin Transformer.
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
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7 authors.
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Abstract
Background: Achieving intelligent detection and grading of lesion cells in ThinPrep cytologic test (TCT) pathological images is challenging but may provide considerable clinical value in improving the accuracy of early cervical cancer screening. Therefore, we sought to design a rapid and accurate method for the fine-grained detection of cervical lesions in massive TCT images. Methods: We developed a YoGaNet architecture based on the YOLOv5l network and dropped multibranch Swin Transformer (DMBST) module. This architecture extracts multilevel global and local features from images, whereas the DMBST module directs the network to learn different features and enhances the capability of fine-grained feature extraction of small objects. The performance of YoGaNet was evaluated on the large public Comparison Detector dataset, containing 7,410 cervical microscopy images and 11 lesion cell categories, and a dataset of a clinical study, which retrospectively enrolled 12 patients with 1,514 cervical microscopy images. Results: In the experiments, YoGaNet achieved the highest mean average precision calculated at an intersection over a union threshold of 0.50 (mAP50) (Comparison Detector dataset: 68.6%; clinical dataset: 36.8%). Compared with those for the Comparison Detector dataset provided in the Liang's reported and the baseline model (YOLOv5l), the mAP50 and recall of YoGaNet improved by 22.8% and 3.2% and by 6.2% and 3.3%, respectively. Moreover, YoGaNet provided considerable advantages in detecting atypical squamous cells of undetermined significance (ASC-US), atypical squamous cells, cannot exclude high-grade squamous intraepithelial lesions (ASCHs), low-grade squamous intraepithelial lesions (LSILs), high-grade squamous intraepithelial lesions (HSILs), squamous cell carcinoma (SCC), Conclusions: YoGaNet improved the fine-grained recognition of cervical cells in massive TCT images as compared with the baseline model and models reported in previous studies and may thus aid in improving early cervical cancer diagnosis. The inference code is available online (https://github.com/coding-with-chen/YoGA-Net-DMBST).
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