Al Zegair Fatima, Betz-Stablein Brigid, Janda Monika, Soyer H Peter, Chandra Shekhar S
School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD, Australia.
Dermatology Research Centre, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia.
Phys Eng Sci Med. 2025 Sep 17. doi: 10.1007/s13246-025-01636-9.
A naevus is a benign melanocytic skin tumour made up of naevus cells, characterised by variations in size, shape, and colour. Understanding naevi is essential due to their significant role as markers for the risk of developing melanoma. This study focused on creating a visual representation called a manifold that illustrates the distribution of two types of naevi: suspicious and non-suspicious. The research aimed to classify real naevi using generative adversarial networks (GANs), while also generating realistic synthetic samples and interpreting their distribution through a variational manifold. This inquiry holds promise for applying data-driven methods for early melanoma detection by identifying distinct features linked with suspicious naevi. Our variational autoencoder auxiliary classifier generative adversarial network (VAE-ACGAN) for suspicious naevi revealed a manifold with outstanding performance, including specificity, sensitivity, and area under the curve (AUC) scores, particularly representing suspicious naevi. These models surpassed various deep learning frameworks in key performance metrics while producing a manifold that indicated a significant distinction between the two categories in the resultant image, yielding high-quality and life-like representations of naevi. The results highlight the potential application of GANs in expanding data sets and enhancing the effectiveness of deep learning algorithms in dermatology. Accurate identification and categorisation of naevi could facilitate early melanoma detection and deepen our understanding of these skin lesions through an interpretable clustering method based on visual similarities.
痣是一种由痣细胞组成的良性黑素细胞性皮肤肿瘤，其特征在于大小、形状和颜色的变化。由于痣作为黑色素瘤发生风险的标志物具有重要作用，因此了解痣至关重要。本研究专注于创建一种称为流形的可视化表示，以说明两种类型的痣（可疑痣和非可疑痣）的分布情况。该研究旨在使用生成对抗网络（GAN）对真实的痣进行分类，同时生成逼真的合成样本，并通过变分流形解释其分布。这项研究有望通过识别与可疑痣相关的独特特征，将数据驱动方法应用于早期黑色素瘤检测。我们用于可疑痣的变分自编码器辅助分类器生成对抗网络（VAE - ACGAN）揭示了一个具有出色性能的流形，包括特异性、敏感性和曲线下面积（AUC）得分，尤其能够代表可疑痣。这些模型在关键性能指标上超越了各种深度学习框架，同时生成了一个流形，该流形在生成的图像中显示出两类之间的显著差异，产生了高质量且逼真的痣的表示。结果突出了GAN在扩展数据集和提高皮肤病学深度学习算法有效性方面的潜在应用。通过基于视觉相似性的可解释聚类方法，对痣进行准确识别和分类有助于早期黑色素瘤检测，并加深我们对这些皮肤病变的理解。
