Fig. 4: Using SemanticLens to find and correct bugs in medical models that detect melanoma skin cancer.
From: Mechanistic understanding and validation of large AI models with SemanticLens
a, The ABCDE rule is a popular guide to visual melanoma clues. We expect models to learn several concepts corresponding to the ABCDE rule, as well as other melanoma-unrelated indications (such as regular border) or spurious concepts, including hairs or a band-aid. b, In a semantic space visualized with a UMAP projection, we can identify valid concepts, such as blue-white veil for ‘melanoma’, but also spurious ones such as red skin or ruler. c, When investigating the importance of concepts, red skin or band-aid concepts are strongly used for the ‘other’ (non-melanoma) class. Ruler concepts are used with slightly higher relevance for ‘melanoma’. d, We can improve the safety and robustness of our model either by changing the model and removing spurious components or by retraining it on augmented data. Whereas both approaches lead to improved clean performance, the influence of artefacts is only substantially reduced through retraining. Images in a, b and d are adapted with permission from ref. 28 under a Creative Commons license CC BY 4.0 and ref. 39 under a Creative Commons license CC BY 4.0.
