4 citations
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April 2024 in “Complex & Intelligent Systems” This study introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.
8 citations
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February 2019 in “Scientific Reports” This article describes the immunofluorescence tomography method to achieve high-resolution 3-D reconstruction of epithelial tissues and reports no clinical results.
July 2025 in “Journal of Neonatal Surgery” This study utilized U-Net's image-processing capabilities to achieve 92% accuracy in segmenting individual hair strands, enhancing early detection and reliable identification of hair fall areas, which assists in addressing challenges of subtle hair thinning that are difficult to see otherwise.
January 2024 in “International Journal of Advanced Computer Science and Applications” This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.
April 2019 in “The journal of investigative dermatology/Journal of investigative dermatology” This study found that low image resolutions allow expert clinicians to detect alopecia, but higher resolutions are necessary for identifying scarring and vellus hair, which may inform future image processing algorithms in dermatology.