CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions

• URL: http://arxiv.org/abs/2208.13528v1
• Date: Mon, 29 Aug 2022 12:06:10 GMT
• Title: CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions
• Authors: Arezou Pakzad, Kumar Abhishek, Ghassan Hamarneh
• Abstract summary: 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.
• Score: 16.65329510916639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While deep learning based approaches have demonstrated expert-level performance in dermatological diagnosis tasks, they have also been shown to exhibit biases toward certain demographic attributes, particularly skin types (e.g., light versus dark), a fairness concern that must be addressed. We propose CIRCLe, a skin color invariant deep representation learning method for improving fairness in skin lesion classification. CIRCLe is trained to classify images by utilizing a regularization loss that encourages images with the same diagnosis but different skin types to have similar latent representations. Through extensive evaluation and ablation studies, 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, using classification accuracy, equal opportunity difference (for light versus dark groups), and normalized accuracy range, a new measure we propose to assess fairness on multiple skin type groups.

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)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• 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)
• Revisiting Skin Tone Fairness in Dermatological Lesion Classification [3.247628857305427]
  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.
  arXiv  Detail & Related papers  (2023-08-18T15:59:55Z)
• FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning [11.883809920936619]
  We propose FairDisCo, a disentanglement deep learning framework with contrastive learning. We compare FairDisCo to three fairness methods, namely, resampling, reweighting, and attribute-aware. We adapt two fairness-based metrics DPM and EOM for our multiple classes and sensitive attributes task, highlighting the skin-type bias in skin lesion classification.
  arXiv  Detail & Related papers  (2022-08-22T01:54:23Z)
• Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification [4.414962444402826]
  It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. In this paper, we revisit the significance of shape-biases for the classification of skin lesion images.
  arXiv  Detail & Related papers  (2022-06-13T20:59:06Z)
• 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)
• EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation [9.750368551427494]
  Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assisting clinicians with prescreening patients. These models often learn biases inherent in training data, which can lead to a performance gap in the diagnosis of people with light and/or dark skin tones.
  arXiv  Detail & Related papers  (2022-02-28T15:33:31Z)
• 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.