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Related Concept Videos

Fixation and Sectioning01:03

Fixation and Sectioning

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Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...
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Related Experiment Video

Updated: Jan 9, 2026

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
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StainExpert: A Unified Multi-Expert Diffusion Framework for Multi-Target Pathological Stain Translation.

Zeyu Liu, Yufang He, Tianyi Zhang

    IEEE Transactions on Medical Imaging
    |December 8, 2025
    PubMed
    Summary

    StainExpert is a new AI framework that generates multiple virtual pathology stains from a single image. This computational pathology tool streamlines diagnostics, saves tissue, and improves efficiency.

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    Area of Science:

    • Computational pathology
    • Digital pathology
    • Artificial intelligence in medicine

    Background:

    • Histopathological analysis is crucial for disease diagnosis, using various staining methods like hematoxylin and eosin (H&E), special stains, immunohistochemistry (IHC), and multiplex immune-fluorescence (mpIF).
    • Current sequential staining methods are time-consuming, labor-intensive, consume significant tissue, and can compromise sample integrity due to serial sectioning.

    Purpose of the Study:

    • To introduce StainExpert, a unified multimodal diffusion framework for translating a single source pathological stain image into multiple target stain images.
    • To develop a novel multi-expert system that enables efficient, single-source to multi-target stain translation, overcoming limitations of single-pair models.

    Main Methods:

    • Developed StainExpert, a multimodal diffusion framework with a multi-expert system for collaborative learning of staining principles.
    • Employed multi-expert and multi-objective optimization for efficient source-to-multi-target translation.
    • Integrated textual guidance with visual features within a multimodal diffusion architecture.

    Main Results:

    • StainExpert successfully generated high-quality virtual stains across H&E, special stains, IHC, and mpIF modalities on three datasets.
    • The generated virtual stains preserved critical pathological features essential for accurate diagnosis.
    • The framework demonstrated robust cross-domain generalization capabilities.

    Conclusions:

    • StainExpert offers a transformative platform for efficient multi-target pathological stain translation.
    • The tool advances computational pathology towards streamlined, tissue-conserving, and resource-efficient diagnostic workflows.
    • This approach enhances diagnostic accuracy and efficiency in histopathology.