Researchers have identified significant concept-level inconsistencies within the Derm7pt dermoscopy dataset, which limit the accuracy of Concept Bottleneck Models (CBMs). By applying rough set theory, they found that 16.4% of concept profiles are associated with conflicting diagnoses, theoretically capping CBM accuracy at 92.1%. The study also proposes a filtered, consistent subset called Derm7pt+, demonstrating improved CBM performance with various backbone architectures. AI
IMPACT Highlights dataset quality issues impacting model interpretability and accuracy, suggesting data curation is key for reliable CBMs.
RANK_REASON Academic paper analyzing dataset inconsistencies and their impact on model accuracy.
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