Exploring the Impact of Skin Color on Skin Lesion Segmentation
Skin cancer, particularly melanoma, has emerged as a significant cause of morbidity and mortality worldwide, rendering early detection crucial. In the realm of dermatology, artificial intelligence (AI) systems have increasingly relied on skin lesion segmentation as a preprocessing step. This process delineates the lesion from the surrounding skin and supports subsequent analytical tasks. However, while fairness concerns regarding skin tone have been extensively studied in lesion classification, the influence of skin tone on the segmentation stage remains insufficiently quantified.
Research Overview
Recent work, documented in arXiv:2603.29694v1, investigates the performance of three prominent segmentation architectures: UNet, DeepLabV3 with a ResNet50 backbone, and DINOv2. This evaluation is conducted across two public dermoscopic datasets—HAM10000 and ISIC2017. A novel continuous pigment or contrast analysis is introduced, treating pixel-wise Individual Typology Angle (ITA) values as distributions. This approach facilitates a deeper understanding of how skin tone influences segmentation outcomes.
Methodology
The research employs Wasserstein distances to compare within-image distributions for skin-only, lesion-only, and whole-image regions. This quantification of lesion-skin contrast is then correlated with segmentation performance across multiple metrics. By analyzing the data, the study aims to uncover the relationship between skin tone and segmentation accuracy.
Key Findings
- Global skin tone metrics, such as Fitzpatrick grouping or mean ITA, exhibit a weak association with segmentation quality.
- Low lesion-skin contrast is consistently linked to larger segmentation errors in the models tested.
- Boundary ambiguity and low contrast are identified as significant factors driving segmentation failure.
Implications for Fairness in Dermatology
The findings of this study suggest that improvements in fairness regarding dermoscopic segmentation should focus on the robust handling of low-contrast lesions. The traditional discrete categorization of skin tones may not provide sufficient insight into the complexities of segmentation performance. Instead, the distribution-based pigment measures offer a more nuanced and informative audit signal.
Conclusion
This research highlights the necessity of addressing the impact of skin color on segmentation processes within AI-driven dermatology systems. By prioritizing the understanding of low-contrast lesions and employing more sophisticated measures of skin tone, the field can advance towards more equitable and effective diagnostic tools. The implications extend beyond mere classification, prompting a reevaluation of how dermatological AI systems are trained and assessed.
As AI technology continues to evolve, it is imperative that researchers and practitioners remain vigilant to ensure fairness and accuracy in the development of dermatological tools that could ultimately save lives.