Effect of Demographic Bias on Skin Lesion Classification
The study investigates the impact of demographic bias on skin lesion classification using ResNet-based models. It highlights that sex-specific training datasets can optimize model performance, while age biases favor younger groups. The research also emphasizes the need for targeted strategies to mitigate these biases in machine learning applications.
- ▪The study evaluates skin lesion classification performance focusing on demographic bias related to patient sex and age.
- ▪Sex-specific training datasets improved model performance, particularly for male patients in female-majority cases.
- ▪Reinforcing and adversarial learning schemes reduced bias gaps in balanced datasets but were less effective in male-majority settings.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.03214 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | TCqvs-mJpL85 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2606.03214 (cs) [Submitted on 2 Jun 2026] Title:Effect of Demographic Bias on Skin Lesion Classification Authors:Ralf Raumanns, Gerard Schouten, Veronika Cheplygina, Josien P.W. Pluim View a PDF of the paper titled Effect of Demographic Bias on Skin Lesion Classification, by Ralf Raumanns and Gerard Schouten and Veronika Cheplygina and Josien P.W. Pluim View PDF HTML (experimental) Abstract:In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic bias in training data, particularly variations in patient sex and age.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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