April 2026 in “Scientific Reports” In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.
April 2023 in “Journal of Investigative Dermatology” This study reports that improvements to the EczemaNet pipeline, incorporating pixel-level segmentation and data augmentation, enhanced the reliability and interpretability of assessing atopic dermatitis severity from digital images.
This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.
September 2023 in “Journal of the American Academy of Dermatology” The model can effectively identify good quality skin images but needs more testing for real-world use.
87 citations
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December 2004 in “Dermatology” This review discusses the term "skin pore," its different meanings, and current methods to objectively describe skin pores, but reports no new clinical findings.
