Skin lesion analysis models are biased by artifacts placed during image acquisition, which influence model predictions despite carrying no clinical information. Solutions that address this problem by regularizing models to prevent learning those spurious features achieve only partial success, and existing test-time debiasing techniques are inappropriate for skin lesion analysis due to either making unrealistic assumptions on the distribution of test data or requiring laborious annotation from medical practitioners. We propose TTS (Test-Time Selection), a human-in-the-loop method that leverages positive (e.g., lesion area) and negative (e.g., artifacts) keypoints in test samples. TTS effectively steers models away from exploiting spurious artifact-related correlations without retraining, and with less annotation requirements. Our solution is robust to a varying availability of annotations, and different levels of bias. We showcase on the ISIC2019 dataset (for which we release a subset of annotated images) how our model could be deployed in the real-world for mitigating bias.

通过利用人-机循环方法中的正（如病变区域）和负（如图像伪影）关键点，我们提出了一种名为TTS（测试时选择）的方法，该方法可以在不重新训练的情况下有效地使模型避免利用伪影相关的关联，并具有较少的标注需求，从而解决了皮肤病变分析模型受到图像伪影的偏见影响的问题。我们展示了在ISIC2019数据集上的应用，演示了我们的模型是如何在现实世界中减轻偏见的。

鲁棒皮肤病变分析的测试时间选择