Concept inconsistency in dermoscopic concept bottleneck models: a rough-set analysis of the Derm7pt dataset.

Concept Bottleneck Models (CBMs) are devoted to melanoma classification route predictions through a clinically grounded concept layer, which binds interpretability to concept-label consistency. When a dataset contains concept-level inconsistencies, identical concept profiles mapped to conflicting diagnosis labels create an unresolvable bottleneck that imposes a hard ceiling on achievable accuracy. In this paper, we apply rough set theory to the Derm7pt dermoscopy benchmark and characterize, for the first time, the full extent and clinical structure of this inconsistency. Among 305 unique concept profiles formed by the dermoscopic criteria of the 7-point melanoma checklist, 50 (16.4%) are inconsistent and span 306 images (30.3% of the dataset). This yields a theoretical accuracy ceiling of 92.1% for any hard CBM trained on the full raw data, where over half of all melanoma images carry concept signatures belonging to the boundary region. This disproportionate fraction shows that the checklist concepts are less discriminative for melanoma than for non-melanoma lesions. We characterize the conflict-severity distribution and identify the clinical features most responsible for boundary ambiguity. Two filtering strategies are proposed to remove images with inconsistent concept signatures, both producing a benchmark that we refer to as Derm7pt+. The symmetric strategy yields a fully consistent subset of 705 images, while the asymmetric strategy retains all melanoma images and removes only the conflicting non-melanoma counterparts, producing 841 images. We evaluate a hard CBM across 19 backbone architectures from the EfficientNet, DenseNet, ResNet, and Wide ResNet families on both Derm7pt+ variants. Under symmetric filtering, DenseNet-169 achieves the best test macro F1 of [Formula: see text], and EfficientNet-B4 leads under asymmetric filtering with a test macro F1 of [Formula: see text]. Macro-averaged concept accuracy remains moderate yet stable across all configurations, and this level of concept prediction suffices to produce solid label macro F1 scores. This shows that annotation noise rather than backbone capacity is the binding constraint on bottleneck quality. We further apply the Sparseness-Optimized Feature Importance (SOFI) explainer to the true positive melanoma predictions of the best-performing model on the symmetric Derm7pt+. We found that irregular dots and globules drives every melanoma prediction, while irregular streaks, atypical pigment network, and present blue-whitish veil form a consistent secondary tier.