Abstract
Addressing the challenges of rare diseases is difficult, especially with the limited number of reference images and a small patient population. This is more evident in rare skin diseases, where we encounter long-tailed data distributions that make it difficult to develop unbiased and broadly effective models. The diverse ways in which image datasets are gathered and their distinct purposes also add to these challenges. Our study conducts a detailed examination of the benefits and drawbacks of episodic and conventional training methodologies, adopting a few-shot learning approach alongside transfer learning. We evaluated our models using the ISIC2018, Derm7pt, and SD-198 datasets. With minimal labeled examples, our models showed substantial information gains and better performance compared to previously trained models. Our research emphasizes the improved ability to represent features in DenseNet121 and MobileNetV2 models, achieved by using pre-trained models on ImageNet to increase similarities within classes. Moreover, our experiments, ranging from 2-way to 5-way classifications with up to 10 examples, showed a growing success rate for traditional transfer learning methods as the number of examples increased. The addition of data augmentation techniques significantly improved our transfer learning based model performance, leading to higher performances than existing methods, especially in the SD-198 and ISIC2018 datasets. All source code related to this work will be made publicly available soon at the provided URL.
Abstract (translated)
解决罕见疾病面临的挑战是困难的，尤其是在参考图像数量有限且患者人口规模较小的情况下。这在罕见皮肤疾病中更加明显，因为我们会遇到具有长尾数据分布的疾病，这使得开发无偏差且具有广泛效果的模型变得困难。图像数据集的收集方式和它们的独特目的也增加了这些挑战。我们的研究详细探讨了周期性训练方法和传统训练方法的优缺点，并采用少量样本学习方法与迁移学习相结合。我们使用ISIC2018、Derm7pt和SD-198数据集来评估我们的模型。由于样本标注数量很少，我们的模型在性能上与之前训练的模型相比取得了很大的信息和特征增益。我们的研究重点是改善DenseNet121和MobileNetV2模型的特征表示能力，通过在ImageNet上预训练模型来增加类内相似度。此外，我们的实验，从2-way到5-way分类，有 up to 10 个样本，表明随着样本数量的增加，传统迁移学习方法的转移学习效果逐渐提高。数据增强技术极大地提高了基于模型的迁移学习性能，特别是在SD-198和ISIC2018数据集上，使得现有方法的性能更优。所有与本研究相关的源代码都将很快在提供的URL上公开发布。
URL
https://arxiv.org/abs/2404.16814
