Revisiting Skin Tone Fairness in Dermatological Lesion Classification

• URL: http://arxiv.org/abs/2308.09640v1
• Date: Fri, 18 Aug 2023 15:59:55 GMT
• Title: Revisiting Skin Tone Fairness in Dermatological Lesion Classification
• Authors: Thorsten Kalb, Kaisar Kushibar, Celia Cintas, Karim Lekadir, Oliver Diaz, Richard Osuala
• Abstract summary: We review and compare four ITA-based approaches of skin tone classification on the ISIC18 dataset. Our analyses reveal a high disagreement among previously published studies demonstrating the risks of ITA-based skin tone estimation methods. We investigate the causes of such large discrepancy among these approaches and find that the lack of diversity in the ISIC18 dataset limits its use as a testbed for fairness analysis.
• Score: 3.247628857305427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Addressing fairness in lesion classification from dermatological images is crucial due to variations in how skin diseases manifest across skin tones. However, the absence of skin tone labels in public datasets hinders building a fair classifier. To date, such skin tone labels have been estimated prior to fairness analysis in independent studies using the Individual Typology Angle (ITA). Briefly, ITA calculates an angle based on pixels extracted from skin images taking into account the lightness and yellow-blue tints. These angles are then categorised into skin tones that are subsequently used to analyse fairness in skin cancer classification. In this work, we review and compare four ITA-based approaches of skin tone classification on the ISIC18 dataset, a common benchmark for assessing skin cancer classification fairness in the literature. Our analyses reveal a high disagreement among previously published studies demonstrating the risks of ITA-based skin tone estimation methods. Moreover, we investigate the causes of such large discrepancy among these approaches and find that the lack of diversity in the ISIC18 dataset limits its use as a testbed for fairness analysis. Finally, we recommend further research on robust ITA estimation and diverse dataset acquisition with skin tone annotation to facilitate conclusive fairness assessments of artificial intelligence tools in dermatology. Our code is available at https://github.com/tkalbl/RevisitingSkinToneFairness.

Related papers

• FairSkin: Fair Diffusion for Skin Disease Image Generation [54.29840149709033]
  Diffusion Model (DM) has become a leading method in generating synthetic medical images, but it suffers from a critical twofold bias. We propose FairSkin, a novel DM framework that mitigates these biases through a three-level resampling mechanism. Our approach significantly improves the diversity and quality of generated images, contributing to more equitable skin disease detection in clinical settings.
  arXiv  Detail & Related papers  (2024-10-29T21:37:03Z)
• Evaluating Machine Learning-based Skin Cancer Diagnosis [0.0]
  The research assesses two convolutional neural network architectures: a MobileNet-based model and a custom CNN model. Both models are evaluated for their ability to classify skin lesions into seven categories and to distinguish between dangerous and benign lesions. The study concludes that while the models show promise in explainability, further development is needed to ensure fairness across different skin tones.
  arXiv  Detail & Related papers  (2024-09-04T02:44:48Z)
• DDI-CoCo: A Dataset For Understanding The Effect Of Color Contrast In Machine-Assisted Skin Disease Detection [51.92255321684027]
  We study the interaction between skin tone and color difference effects and suggest that color difference can be an additional reason behind model performance bias between skin tones. Our work provides a complementary angle to dermatology AI for improving skin disease detection.
  arXiv  Detail & Related papers  (2024-01-24T07:45:24Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions [16.65329510916639]
  We propose CIRCLe, a skin color invariant deep representation learning method for improving fairness in skin lesion classification. We demonstrate CIRCLe's superior performance over the state-of-the-art when evaluated on 16k+ images spanning 6 Fitzpatrick skin types and 114 diseases.
  arXiv  Detail & Related papers  (2022-08-29T12:06:10Z)
• Meta Balanced Network for Fair Face Recognition [51.813457201437195]
  We systematically and scientifically study bias from both data and algorithm aspects. We propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss. Extensive experiments show that MBN successfully mitigates bias and learns more balanced performance for people with different skin tones in face recognition.
  arXiv  Detail & Related papers  (2022-05-13T10:25:44Z)
• Automatic Facial Skin Feature Detection for Everyone [60.31670960526022]
  We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild. To be specific, we annotate the locations of acne, pigmentation, and wrinkle for selfie images with different skin tone colors, severity levels, and lighting conditions.
  arXiv  Detail & Related papers  (2022-03-30T04:52:54Z)
• Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification [5.71097144710995]
  We use a modified variational autoencoder to uncover skin tone bias in datasets commonly used as benchmarks. We propose an efficient yet effective algorithm for automatically labelling the skin tone of lesion images. We subsequently use two leading bias unlearning techniques to mitigate skin tone bias.
  arXiv  Detail & Related papers  (2022-02-06T18:53:06Z)
• Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset [0.23746609573239755]
  This dataset includes 16,577 clinical images sourced from two dermatology atlases with Fitzpatrick skin type labels. We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on.
  arXiv  Detail & Related papers  (2021-04-20T13:37:30Z)
• Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification [76.89093364969253]
  We propose to apply Few-Shot Learning to skin disease identification to address the extreme scarcity of training sample problem. Based on a detailed analysis, we propose the Query-Relative (QR) loss, which proves superior to Cross Entropy (CE) under episode training. We further strengthen the proposed QR loss with a novel adaptive hard margin strategy.
  arXiv  Detail & Related papers  (2020-04-21T00:57:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.