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Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Related Experiment Video

Updated: Apr 2, 2026

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ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality.

Matias Cosarinsky, Ramiro Billot, Lucas Mansilla

    IEEE Transactions on Medical Imaging
    |March 31, 2026
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    This study introduces Conformal In-Context RCA (ConfIC-RCA), a novel method for automated medical image segmentation quality assessment without ground truth. It provides statistically guaranteed quality estimates, enhancing clinical workflow reliability.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Assessing automatic image segmentation quality is vital in clinical settings but hindered by limited ground truth annotations.
    • Reverse Classification Accuracy (RCA) estimates segmentation quality by training a segmenter on new predictions and evaluating against existing annotations.

    Purpose of the Study:

    • To introduce ConfIC-RCA, a novel method for automated segmentation quality estimation with statistical guarantees, even without ground truth.
    • To address the challenge of reliable quality assessment in clinical workflows.

    Main Methods:

    • Developed In-Context RCA, utilizing in-context learning models and retrieval augmentation for efficient quality estimation with minimal reference data.
    • Introduced Conformal RCA, extending RCA and In-Context RCA using split conformal prediction to provide statistically guaranteed prediction intervals for segmentation quality.

    Main Results:

    • ConfIC-RCA demonstrated robust performance and computational efficiency across 10 diverse medical imaging tasks.
    • The method provides reliable quality estimation and statistical guarantees, crucial for automated quality control.

    Conclusions:

    • ConfIC-RCA offers a promising solution for automated quality control in clinical workflows by enabling fast and reliable segmentation assessment.
    • The approach enhances the trustworthiness of AI-driven segmentation in medical practice.