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Related Experiment Video

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and

Yuan Zhang, Xinfeng Zhang, Xiaoming Qi

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    This summary is machine-generated.

    Content generation models in computational pathology aid learning and data augmentation. This review synthesizes progress in image, text, and molecular data generation, highlighting future directions for clinical applications.

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    Area of Science:

    • Computational Pathology
    • Artificial Intelligence in Medicine

    Background:

    • Content generation modeling is a rapidly advancing field in computational pathology.
    • It offers significant potential for data-efficient learning, synthetic data augmentation, and task-oriented generation.

    Purpose of the Study:

    • To provide a comprehensive review of recent progress in content generation for computational pathology.
    • To synthesize key developments, datasets, evaluation protocols, and limitations in the field.

    Main Methods:

    • Systematic analysis of over 150 representative studies in content generation for computational pathology.
    • Categorization of research into image generation, text generation, molecular profile-morphology generation, and specialized applications.

    Main Results:

    • Evolution of architectures from Generative Adversarial Networks (GANs) to diffusion models and vision-language models.
    • Identification of common datasets and evaluation metrics.
    • Highlighting limitations such as high-fidelity whole slide image generation and clinical interpretability.

    Conclusions:

    • Content generation holds promise for computational pathology but faces challenges in fidelity, interpretability, and ethical considerations.
    • Future research should focus on integrated, clinically deployable generation systems.