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ConRad framework enhances conformal prediction for medical radiomics

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ConRad, a new framework for conformal prediction in radiomics that aims to improve the efficiency and reliability of measurements derived from medical images. ConRad addresses the issue of overconfident or poorly calibrated segmentation models by constructing adaptive prediction intervals that incorporate test-time information such as image appearance, mask geometry, and segmentation uncertainty. Experiments across multiple datasets and radiomic targets demonstrate that ConRad achieves better feature-level efficiency while maintaining coverage guarantees, with segmentation boundary uncertainty identified as a key factor in its performance. AI

IMPACT Enhances reliability of AI-driven medical image analysis by improving calibration of segmentation models.

RANK_REASON The cluster contains a research paper detailing a new method for conformal prediction in radiomics.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ConRad framework enhances conformal prediction for medical radiomics

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The cluster contains a research paper detailing a new method for conformal prediction in radiomics.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan ·

    ConRad: Efficient Conformal Prediction for Radiomics

    arXiv:2607.08084v1 Announce Type: cross Abstract: Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines. In practice, these features are often computed from predicted masks, but segmentation models c…

  2. arXiv cs.CV TIER_1 English(EN) · Guha Balakrishnan ·

    ConRad: Efficient Conformal Prediction for Radiomics

    Radiomic features derived from medical images and segmentation masks are used to support decision making in clinical imaging pipelines. In practice, these features are often computed from predicted masks, but segmentation models can be overconfident or poorly calibrated, making d…

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