It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. This assumption has led to a growing body of work aiming to align deep models' decision-making processes with the fundamental properties of human vision. The reliance on shape features is primarily expected to improve the robustness of these models under covariate shift. In this paper, we revisit the significance of shape-biases for the classification of skin lesion images. Our analysis shows that different skin lesion datasets exhibit varying biases towards individual image features. Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation. This indicates that these features are still represented in the learnt embedding spaces of the models, but not used for classification. In addition, the spectral analysis of different datasets shows that in contrast to common visual recognition, dermoscopic skin lesion classification, by nature, is reliant on complex feature combinations beyond shape-bias. As a natural consequence, shifting away from the prevalent desire of shape-biasing models can even improve skin lesion classifiers in some cases.

本文通过对不同皮肤病例数据集的分析，指出了在皮肤病图像分类方面，深层模型中的shape-bias标准并不一定适用。实验结果表明，皮肤病图像的分类并非基于个别特征，而是需要考虑诸多特征的组合。因此，不使用shape-bias标准有时候甚至可以提高皮肤病分类器的性能。

深度学习在皮肤镜皮肤病分类中的形状偏差再探讨