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      research MSF-VMDNet for multi class segmentation of skin cancer whole slide images using a multi frequency dual encoder network

      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.
      Correspondence

      research Correspondence

      November 1998 in “Journal of The European Academy of Dermatology and Venereology”
      A man's skin cancer improved and some of his hair grew back after treatment with a special light therapy and a medication.
      Interferons in Dermatology

      research INTERFERONS IN DERMATOLOGY

      22 citations , April 1998 in “Dermatologic Clinics”
      Interferons are effective for some skin conditions and cancers, but can have side effects and need more research for optimal use.

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