Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918133497856000 |
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| author | Paxton, Kuniko Aslansefat, Koorosh Thakker, Dhavalkumar Papadopoulos, Yiannis Maslekar, Tanaya |
| author_facet | Paxton, Kuniko Aslansefat, Koorosh Thakker, Dhavalkumar Papadopoulos, Yiannis Maslekar, Tanaya |
| contents | Recent advances in deep learning have significantly improved the accuracy of skin lesion classification models, supporting medical diagnoses and promoting equitable healthcare. However, concerns remain about potential biases related to skin color, which can impact diagnostic outcomes. Ensuring fairness is challenging due to difficulties in classifying skin tones, high computational demands, and the complexity of objectively verifying fairness. To address these challenges, we propose a fairness algorithm for skin lesion classification that overcomes the challenges associated with achieving diagnostic fairness across varying skin tones. By calculating the skewness of the feature map in the convolution layer of the VGG (Visual Geometry Group) network and the patches and the heads of the Vision Transformer, our method reduces unnecessary channels related to skin tone, focusing instead on the lesion area. This approach lowers computational costs and mitigates bias without relying on conventional statistical methods. It potentially reduces model size while maintaining fairness, making it more practical for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00745 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Paxton, Kuniko Aslansefat, Koorosh Thakker, Dhavalkumar Papadopoulos, Yiannis Maslekar, Tanaya Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning Recent advances in deep learning have significantly improved the accuracy of skin lesion classification models, supporting medical diagnoses and promoting equitable healthcare. However, concerns remain about potential biases related to skin color, which can impact diagnostic outcomes. Ensuring fairness is challenging due to difficulties in classifying skin tones, high computational demands, and the complexity of objectively verifying fairness. To address these challenges, we propose a fairness algorithm for skin lesion classification that overcomes the challenges associated with achieving diagnostic fairness across varying skin tones. By calculating the skewness of the feature map in the convolution layer of the VGG (Visual Geometry Group) network and the patches and the heads of the Vision Transformer, our method reduces unnecessary channels related to skin tone, focusing instead on the lesion area. This approach lowers computational costs and mitigates bias without relying on conventional statistical methods. It potentially reduces model size while maintaining fairness, making it more practical for real-world applications. |
| title | Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2509.00745 |