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    Area of Science:

    • Biomedical image analysis
    • Computational biology
    • Machine learning in life sciences

    Background:

    • Increasing volumes of biomedical image data necessitate advanced analysis techniques.
    • Many biological questions require focused analysis of specific image sub-regions (regions of interest, ROIs).
    • Current methods can be computationally intensive, requiring optimization for efficiency.

    Purpose of the Study:

    • To develop an efficient, three-step pipeline for analyzing biomedical images.
    • To reduce computational load by analyzing ROIs at varying resolutions.
    • To improve the accuracy of quantitative measurements by focusing on high-confidence sub-regions.

    Main Methods:

    • Low-resolution identification of regions of interest (ROIs) using deep learning.
    • Mid-resolution semantic segmentation of ROIs with confidence estimation via conformal prediction.
    • Full-resolution quantitative measurements on high-confidence segmented sub-regions.

    Main Results:

    • The proposed pipeline effectively locates ROIs and segments them into biologically relevant sub-regions.
    • Conformal prediction provides reliable confidence estimates for segmented sub-regions.
    • Limiting quantitative analysis to high-confidence sub-regions significantly reduces noise.
    • Improved separability of observed biological effects was achieved.

    Conclusions:

    • The three-step pipeline offers a computationally efficient and accurate method for biomedical image analysis.
    • Integrating deep learning with conformal prediction enhances the reliability and interpretability of image analysis results.
    • This approach optimizes resource utilization while improving the detection of biological signals in complex image datasets.