Dr. Li Quan Profile

Dr. Li Quan

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Proceedings Article | 13 September 2024 Paper

Enhanced hard negative synthesis for contrastive learning in skin lesion classification

Xue Zhang, Kaida Jiang, Li Quan, Tao Gong

Proceedings Volume 13254, 132541V (2024) https://doi.org/10.1117/12.3039105

KEYWORDS: Skin, Machine learning, Image classification, Data modeling, Projection systems, Statistical modeling

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The analysis of skin lesion dermoscopic images has traditionally relied on supervised learning paradigms, necessitating extensive labeled datasets which can be costly and time-consuming for dermatologists. Conversely, the abundance of unlabeled data offers a promising avenue for self-supervised learning methods in this field. In this work, we introduce a novel strategy for hard negative synthesis that bolsters the efficacy of contrastive learning in skin lesion classification. By strategically down-weighting the contribution of the hardest negative samples during feature-level mixing, our method ensures the neural network prioritizes learning from the most informative and reliable negatives, thereby enhancing the model's feature learning ability. After fine-tuning with a limited set of labeled data, our method demonstrates notable superiority on the ISIC-2016 classification dataset, achieving a 4.29% increase in ROC AUC and a 5.39% increase in F1 score over the standard MoCo-v2 framework.

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