用20个标注样本生成假数据，提升肺结节诊断解释性模型性能
Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
- 用扩散模型生成带属性标注的肺结节图像，仅需20个真实标注样本
- 合成数据使属性预测准确率提升13.4%，诊断准确率提升1.8%
- 适合医疗AI可解释性研究者，解决小样本标注难题
提供人类可理解解释的分类模型能增强临床医生对医学影像诊断中AI的信任与可用性。研究重点是将放射科医生使用的病理相关视觉属性（如形状、纹理）与诊断结果联合建模，使AI决策过程更贴近临床思维。然而，这类模型推广受限于缺乏大规模带属性标注的医学图像数据集。为应对这一挑战，我们提出利用生成模型合成属性标注数据。通过在扩散模型中引入属性条件，仅使用来自LIDC-IDRI数据集的20个带属性标注的肺结节样本进行训练。将生成图像加入可解释模型的训练后，属性预测准确率提高13.4%，目标诊断准确率提升1.8%。本工作展示了合成数据在克服数据局限方面的潜力，提升了可解释模型在医学图像分析中的适用性。
原文摘要 · Abstract (English)
Classification models that provide human-interpretable explanations enhance clinicians' trust and usability in medical image diagnosis. One research focus is the integration and prediction of pathology-related visual attributes used by radiologists alongside the diagnosis, aligning AI decision-making with clinical reasoning. Radiologists use attributes like shape and texture as established diagnostic criteria and mirroring these in AI decision-making both enhances transparency and enables explicit validation of model outputs. However, the adoption of such models is limited by the scarcity of large-scale medical image datasets annotated with these attributes. To address this challenge, we propose synthesizing attribute-annotated data using a generative model. We enhance the Diffusion Model with attribute conditioning and train it using only 20 attribute-labeled lung nodule samples from the LIDC-IDRI dataset. Incorporating its generated images into the training of an explainable model boosts performance, increasing attribute prediction accuracy by 13.4% and target prediction accuracy by 1.8% compared to training with only the small real attribute-annotated dataset. This work highlights the potential of synthetic data to overcome dataset limitations, enhancing the applicability of explainable models in medical image analysis.
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