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AI model performance in medical imaging heavily influenced by annotation quality, study finds

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates the impact of annotation quality on the performance of AI models for pulmonary embolism (PE) segmentation in CT scans. Researchers found that changes in evaluation annotations significantly affected measured segmentation performance, often more so than modifications to the model training process. The study also introduced a human-referenced benchmark, nnPE, which, despite achieving a Dice Similarity Coefficient (DSC) of 0.72, scored lower than individual human annotators in direct comparisons. The findings highlight the critical role of precise and consistent annotations in developing and evaluating medical imaging AI. AI

IMPACT Highlights the need for high-quality, human-referenced annotations to ensure reliable AI model performance in critical medical applications.

RANK_REASON The cluster contains a research paper detailing a new methodology and benchmark for AI model evaluation in medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model performance in medical imaging heavily influenced by annotation quality, study finds

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36 / 100
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The cluster contains a research paper detailing a new methodology and benchmark for AI model evaluation in medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Qihang Sun, Zhongxiao Liu, Bailiang Jian, Shenman Qiu, Jingyuan Wang, Lei Zhang, Lixiang Xie, Jiazhen Pan, Christian Wachinger ·

    Model Effect or Label Effect? Refined Annotations and a Human-Referenced Benchmark for Pulmonary Embolism Segmentation

    arXiv:2608.24486v1 Announce Type: cross Abstract: Purpose: To quantify how evaluation annotations influence measured pulmonary embolism (PE) segmentation performance relative to model training changes, and to establish a human-referenced framework. Materials and Methods: This ret…

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