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<p>Label noise is a common and important issue that would affect the model’s performance in artificial intelligence. This study assessed the effectiveness and potential risks of automated label cleaning using an open-source framework, Cleanlab, in multi-category datasets of fundus photography and optical coherence tomography, with intentionally introduced label noise ranging from 0 to 70%. After six cycles of automatic cleaning, significant improvements are achieved in label accuracies (3.4–62.9%) and dataset quality scores (DQS, 5.1–74.4%). The majority (86.6 to 97.5%) of label errors were accurately modified, with minimal missed (0.5–2.8%) or misclassified (0.4–10.6%). The classification accuracy of RETFound significantly improved by 0.3–52.9% when trained with the datasets after cleaning. We also developed a DQS-guided cleaning strategy to mitigate over-cleaning. Furthermore, external validation on EyePACS and APTOS-2019 datasets boosted label accuracy by 1.3 and 1.8%, respectively. This approach automates label correction, enhances dataset reliability, and strengthens model performance efficiently and safely.</p>

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Efficiency and safety of automated label cleaning on multimodal retinal images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月5日

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  • Tian Lin,
  • Meng Wang,
  • Aidi Lin,
  • Xiaoting Mai,
  • Huiyu Liang,
  • Yih-Chung Tham,
  • Haoyu Chen

摘要

Label noise is a common and important issue that would affect the model’s performance in artificial intelligence. This study assessed the effectiveness and potential risks of automated label cleaning using an open-source framework, Cleanlab, in multi-category datasets of fundus photography and optical coherence tomography, with intentionally introduced label noise ranging from 0 to 70%. After six cycles of automatic cleaning, significant improvements are achieved in label accuracies (3.4–62.9%) and dataset quality scores (DQS, 5.1–74.4%). The majority (86.6 to 97.5%) of label errors were accurately modified, with minimal missed (0.5–2.8%) or misclassified (0.4–10.6%). The classification accuracy of RETFound significantly improved by 0.3–52.9% when trained with the datasets after cleaning. We also developed a DQS-guided cleaning strategy to mitigate over-cleaning. Furthermore, external validation on EyePACS and APTOS-2019 datasets boosted label accuracy by 1.3 and 1.8%, respectively. This approach automates label correction, enhances dataset reliability, and strengthens model performance efficiently and safely.