mlsanityData to 7 Oct 2026

Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification

arXiv 2202.02832 ↗First posted 6 Feb 2022eess.IV cs.CVcited by 23

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Abstract (arXiv)

Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed before widespread deployment. In this work, we propose an efficient yet effective algorithm for automatically labelling the skin tone of lesion images, and use this to annotate the benchmark ISIC dataset. We subsequently use these automated labels as the target for two leading bias unlearning techniques towards mitigating skin tone bias. Our experimental results provide evidence that our skin tone detection algorithm outperforms existing solutions and that unlearning skin tone may improve generalisation and can reduce the performance disparity between melanoma detection in lighter and darker skin tones.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 6 Feb 2022.

Builds on

14 of 26 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  6. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  7. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  8. 2017Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)cited by 549
  9. 2018Learning Not to Learn: Training Deep Neural Networks with Biased Datacited by 255
  10. 2015Simultaneous Deep Transfer Across Domains and Taskscited by 205
  11. 2021Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Datasetcited by 184
  12. 2020A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Contextcited by 146
2 more
  1. 2018Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddingscited by 114
  2. 2019Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasetscited by 29

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 3; 2024: 5; 2025: 7; 2026: 1.

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  1. 2022Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Reviewcited by 27
  2. 2022A Survey on Deep Learning for Skin Lesion Segmentationcited by 22
  3. 2022FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learningcited by 22
  4. 2022CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesionscited by 17
  5. 2024DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformercited by 17
  6. 2024A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysiscited by 14
  7. 2022The (de)biasing effect of GAN-based augmentation methods on skin lesion imagescited by 9
  8. 2023Revisiting Skin Tone Fairness in Dermatological Lesion Classificationcited by 9
  9. 2024Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasetscited by 7
  10. 2024PatchAlign:Fair and Accurate Skin Disease Image Classification by Alignment with Clinical Labelscited by 5
  11. 2023Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classificationcited by 4
  12. 2025DermDiff: Generative Diffusion Model for Mitigating Racial Biases in Dermatology Diagnosiscited by 4
11 more
  1. 2023Assessing the Generalizability of Deep Neural Networks-Based Models for Black Skin Lesionscited by 3
  2. 2024FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classificationcited by 3
  3. 2025Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learningcited by 2
  4. 2022Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Networkcited by 1
  5. 2022Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasetscited by 1
  6. 2025Prompting Medical Vision-Language Models to Mitigate Diagnosis Bias by Generating Realistic Dermoscopic Imagescited by 1
  7. 2025Skin Color Measurement from Dermatoscopic Images: An Evaluation on a Synthetic Datasetcited by 1
  8. 2025Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regressioncited by 1
  9. 2025Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesiscited by 0
  10. 2025Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosiscited by 0
  11. 2026Colorimeter-Supervised Skin Tone Estimation from Dermatoscopic Images for Fairness Auditingcited by 0