November 2024 in “Image Analysis & Stereology” The method improves hair image segmentation accuracy while reducing annotation costs.
February 2026 in “International journal of intelligent engineering and systems” The new method improves hair segmentation in skin images, helping detect skin cancer more accurately.
April 2023 in “Journal of Investigative Dermatology” The improved EczemaNet more reliably and clearly identifies and assesses the severity of atopic dermatitis from photos.
April 2026 in “Scientific Reports” The study introduces MSF-VMDNet, a novel deep learning model designed for multi-class segmentation of skin cancer whole-slide images, addressing the complexity of differentiating 10 distinct tissue classes. This model combines U-Net and Vision Mamba dual encoders to enhance feature extraction and segmentation accuracy. The U-Net encoder uses an improved AFNO spectral decomposition module for high-resolution semantic information, while the Vision Mamba encoder optimizes long-range dependency modeling. The SCConv module fuses features from various frequency domains and spatial levels. MSF-VMDNet outperforms existing methods, achieving an MIoU of 95.37% and a Dice coefficient of 95.11%, and demonstrates strong generalization across multiple datasets.
A new image-based method improves accuracy in measuring hair loss in mice.