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Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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FIESTA: Fourier-Based Semantic Augmentation With Uncertainty Guidance for Enhanced Domain Generalizability in Medical

Kwanseok Oh, Eunjin Jeon, Da-Woon Heo

    IEEE Transactions on Neural Networks and Learning Systems
    |November 3, 2025
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    Summary
    This summary is machine-generated.

    FIESTA, a novel Fourier-based semantic augmentation method, improves single-source domain generalization for medical image segmentation. It enhances model adaptation to diverse data by manipulating frequency components and focusing on ambiguous regions.

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    Area of Science:

    • Medical Image Analysis
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Single-source domain generalization (SDG) in medical image segmentation (MIS) faces challenges with unseen target domains.
    • Existing data augmentation methods in SDG for MIS often neglect crucial details and uncertain regions, leading to segmentation errors.

    Purpose of the Study:

    • To introduce FIESTA, a Fourier-based semantic augmentation method with uncertainty guidance (UG).
    • To enhance MIS performance in an SDG context by addressing limitations of current approaches.

    Main Methods:

    • FIESTA manipulates amplitude and phase components in the frequency domain using a Fourier augmentative transformer (FAT).
    • FAT performs semantic amplitude modulation and utilizes phase spectrum for structural coherence.
    • Uncertainty estimation fine-tunes augmentation, focusing on ambiguous areas.

    Main Results:

    • FIESTA demonstrated superior segmentation performance across three cross-domain scenarios.
    • The method significantly outperformed state-of-the-art SDG approaches.
    • Improved model adaptability to diverse augmented data and focus on high-ambiguity regions were observed.

    Conclusions:

    • FIESTA offers a robust solution for SDG in MIS by leveraging Fourier transform properties.
    • The proposed method enhances segmentation accuracy and model generalization capabilities.
    • FIESTA shows significant potential for improving medical imaging analysis applications.