Go to IEEE EMBS BHI 2025 Conference homepageTowards More Equitable Ulcer Recognition Models: A Dataset of Naturalistic Foot Images from People of Color Living with Diabetes
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Keywords: Diabetic Foot Ulcer, Computer Vision, Image Classification, Segmentation, Health Equity
Abstract: Diabetic foot ulcers, a life-threatening complication of diabetes, take a disproportionate toll on communities of color; however, these communities are currently underrepresented in dermatologic and wound image datasets. Further, many of these datasets were collected under controlled conditions, limiting the transferability of ulcer recognition models to naturalistic settings. In support of more equitable and generalizable computational modeling, we detail our two-year effort to create the first repository of diabetic foot ulcer images collected predominantly from patients of color in naturalistic settings. We conduct an evaluation of state-of-the-art foot ulcer segmentation and classification methods using our dataset of 3,362 foot images collected from 252 patients, and provide evidence that current ulcer recognition models result in insufficient performance: the best performing baseline model (Mask R-CNN) has been previously reported to achieve a Dice score of 90.2%, but achieves only 39.5% on our more naturalistic dataset from patients of color. We propose and evaluate a new pipeline which improves segmentation performance, including an ulcer detection model and a foundational segmentation model (Segment Anything 2 UNet) tailored to communities of color and specifically aiming for naturalistic assessment scenarios. We release our image dataset to support the development of larger, more diverse datasets, and ultimately more equitable models for diabetic foot care.
Track: 3. Imaging Informatics
Registration Id: PGNB9C8VXRL
Submission Number: 106
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