ArticleJournal of digital imaging2023
A Lightweight and Robust Framework for Circulating Genetically Abnormal Cells (CACs) Identification Using 4-Color Fluorescence In Situ Hybridization (FISH) Image and Deep Refined Learning.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
- BDNet: A Real-Time Biomedical Image Denoising Network with Gradient Information Enhancement Loss.Biosensors · 2026Article
- Enhanced FISH Image Classification via CBAM-PPM-Optimized ResNet50 for Precision Cytogenetic Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Simultaneous classification and delineation of seven types of histological growth patterns in lung adenocarcinomas using self-supervised learning and online hard patch mining.Quantitative imaging in medicine and surgery · 2025Article
- CACs Recognition of FISH Images Based on Adaptive Mean Teacher Semi-supervised Learning with Domain-Knowledge Pseudo Label.Journal of imaging informatics in medicine · 2025Article
- An automatic deep learning-based bone mineral density measurement method using X-ray images of children.Quantitative imaging in medicine and surgery · 2025Article
- A combined model integrating deep learning, radiomics, and clinical ultrasound features for predictingFrontiers in endocrinology · 2025Article
- Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases.Frontiers in oncology · 2025Review
- Adapting SAM2 Model from Natural Images for Tooth Segmentation in Dental Panoramic X-Ray Images.Entropy (Basel, Switzerland) · 2024Article
- Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization.Scientific reports · 2024Article
- A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging.Journal of imaging · 2024Article
- An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.Sensors (Basel, Switzerland) · 2024Article
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7 authors.
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Abstract
Circulating genetically abnormal cells (CACs) constitute an important biomarker for cancer diagnosis and prognosis. This biomarker offers high safety, low cost, and high repeatability, which can serve as a key reference in clinical diagnosis. These cells are identified by counting fluorescence signals using 4-color fluorescence in situ hybridization (FISH) technology, which has a high level of stability, sensitivity, and specificity. However, there are some challenges in CACs identification, due to the difference in the morphology and intensity of staining signals. In this concern, we developed a deep learning network (FISH-Net) based on 4-color FISH image for CACs identification. Firstly, a lightweight object detection network based on the statistical information of signal size was designed to improve the clinical detection rate. Secondly, the rotated Gaussian heatmap with a covariance matrix was defined to standardize the staining signals with different morphologies. Then, the heatmap refinement model was proposed to solve the fluorescent noise interference of 4-color FISH image. Finally, an online repetitive training strategy was used to improve the model's feature extraction ability for hard samples (i.e., fracture signal, weak signal, and adjacent signals). The results showed that the precision was superior to 96%, and the sensitivity was higher than 98%, for fluorescent signal detection. Additionally, validation was performed using the clinical samples of 853 patients from 10 centers. The sensitivity was 97.18% (CI 96.72-97.64%) for CACs identification. The number of parameters of FISH-Net was 2.24 M, compared to 36.9 M for the popularly used lightweight network (YOLO-V7s). The detection speed was about 800 times greater than that of a pathologist. In summary, the proposed network was lightweight and robust for CACs identification. It could greatly increase the review accuracy, enhance the efficiency of reviewers, and reduce the review turnaround time during CACs identification.
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