DDI-CoCo: A Dataset For Understanding The Effect Of Color Contrast In
  Machine-Assisted Skin Disease Detection
Ming-Chang Chiu Yingfei Wang Yen-Ju Kuo Pin-Yu Chen
Skin tone as a demographic bias and inconsistent human labeling poses
challenges in dermatology AI. We take another angle to investigate color
contrast's impact, beyond skin tones, on malignancy detection in skin disease
datasets: We hypothesize that in addition to skin tones, the color difference
between the lesion area and skin also plays a role in malignancy detection
performance of dermatology AI models. To study this, we first propose a robust
labeling method to quantify color contrast scores of each image and validate
our method by showing small labeling variations. More importantly, applying our
method to \textit{the only} diverse-skin tone and pathologically-confirmed skin
disease dataset DDI, yields \textbf{DDI-CoCo Dataset}, and we observe a
performance gap between the high and low color difference groups. This
disparity remains consistent across various state-of-the-art (SoTA) image
classification models, which supports our hypothesis. Furthermore, we study the
interaction between skin tone and color difference effects and suggest that
color difference can be an additional reason behind model performance bias
between skin tones. Our work provides a complementary angle to dermatology AI
for improving skin disease detection.