Image Covariates and Label Noise Robustness in Dermatoscopic Classification

Abstract

Medical image classification depends on labels that can contain diagnostic errors. This study compares six training strategies on DermaMNIST, with 7,007 training images in seven classes and synthetic symmetric, asymmetric, and pixel-difficulty-dependent label noise at nominal rates of 10–40%. The comparison comprises 36 noise-family, rate, and seed combinations, using three seeds and a seven-epoch budget per main network. The instance-dependent-minus-symmetric ERM balanced-accuracy gap changes from +0.063 at 10% to −0.061 at 40%; instance dependence is therefore not uniformly harder for this generator. Image-covariate-only noise scores have pooled AUC 0.502, whereas the combined covariate-and-loss estimator has AUC 0.301 compared with 0.832 for the loss-GMM score. The MIND-CT pipeline has higher reported mean balanced accuracy than its MIND and FC variants, but lower overall means than Co-teaching and ERM: 0.244 versus 0.284 and 0.276. At the designated INST-20% endpoint, Co-teaching reaches 0.343 versus 0.303 for MIND-CT. These findings support a scoped comparison of estimator quality and downstream classification, rather than a conclusion about genuine clinical metadata. Inferential results are exploratory because the grid reuses one archive and three seeds, and source records are needed to reconcile the rank diagram and verify implementation details.