This study presents a lightweight pipeline for skin lesion detection, addressing the challenges posed by imbalanced class distribution and subtle or atypical appearances of some lesions. The pipeline is built around a lightweight model that leverages ghosted features and the DFC attention mechanism to reduce computational complexity while maintaining high performance. The model was trained on the HAM10000 dataset, which includes various types of skin lesions. To address the class imbalance in the dataset, the synthetic minority over-sampling technique and various image augmentation techniques were used. The model also incorporates a knowledge-based loss weighting technique, which assigns different weights to the loss function at the class level and the instance level, helping the model focus on minority classes and challenging samples. This technique involves assigning different weights to the loss function on two levels - the class level and the instance level. By applying appropriate loss weights, the model pays more attention to the minority classes and challenging samples, thus improving its ability to correctly detect and classify different skin lesions. The model achieved an accuracy of 92.4%, a precision of 84.2%, a recall of 86.9%, a f1-score of 85.4% with particularly strong performance in identifying Benign Keratosis-like lesions (BKL) and Nevus (NV). Despite its superior performance, the model's computational cost is considerably lower than some models with less accuracy, making it an optimal solution for real-world applications where both accuracy and efficiency are essential.

翻译：本研究提出了一种用于皮肤病变检测的轻量级流水线，解决了类别分布不均衡及部分病变表现细微或非典型所带来的挑战。该流水线围绕一个轻量级模型构建，利用鬼影特征和DFC注意力机制在保持高性能的同时降低计算复杂度。模型在包含多种皮肤病变类型的HAM10000数据集上进行训练。为应对数据集的类别不平衡问题，采用了合成少数类过采样技术及多种图像增强方法。模型还引入了基于知识的损失加权技术，在类别级别和实例级别为损失函数分配不同权重，帮助模型聚焦于少数类及困难样本。通过应用适当的损失权重，模型对少数类和困难样本给予更多关注，从而提升了正确检测和分类不同皮肤病变的能力。该模型实现了92.4%的准确率、84.2%的精确率、86.9%的召回率、85.4%的F1分数，尤其在识别良性角化样病变和痣方面表现突出。尽管性能优异，该模型的计算成本远低于一些精度较低的模型，使其成为在精度和效率均至关重要的实际应用中的最优解决方案。