NEWS · RESEARCH · #235
SCIN: open-access, crowd-contributed dermatology image dataset focused on everyday conditions and diverse skin tones
Google Research and Stanford Medicine released the Skin Condition Image Network (SCIN), an open-access dataset of over 10,000 crowd-contributed images of skin, nail, and hair concerns collected in the US under an IRB-approved study. Images include self-reported demographics and tanning propensity, dermatologist labels (1–3 labelers per contribution with confidence scores and aggregated weighted differentials), and estimated skin type/tone annotations (self-reported sFST, dermatologist-estimated eFST, and layperson eMST); the dataset emphasizes common allergic, inflammatory, and infectious conditions and contains many early-stage presentations and a broader distribution of darker Fitzpatrick skin types than several clinical datasets.
KEY POINTS
- Google Research and Stanford Medicine released the Skin Condition Image Network (SCIN), an open-access dataset of over 10,000 crowd-contributed images of skin, nail, and hair concerns collected in the US under an IRB-approved study.
- Images include self-reported demographics and tanning propensity, dermatologist labels (1–3 labelers per contribution with confidence scores and aggregated weighted differentials), and estimated skin type/tone annotations (self-reported sFST, dermatologist-estimated eFST, and layperson eMST); the dataset emphasizes common allergic, inflammatory, and infectious conditions and contains many early-stage presentations and a broader distribution of darker Fitzpatrick skin types than several clinical datasets.
- SCIN provides a more representative, early-stage and skin-tone-diverse benchmark for training and evaluating dermatology AI models that target common non-neoplastic conditions, addressing gaps in existing clinical datasets.
WHY IT MATTERS
SCIN provides a more representative, early-stage and skin-tone-diverse benchmark for training and evaluating dermatology AI models that target common non-neoplastic conditions, addressing gaps in existing clinical datasets.