使用拉曼光谱 (RS) 信号进行皮肤癌组织分类最近引起了极大的关注，因为其非侵入性光学技术本质是利用生物组织内的分子结构和构象进行诊断。事实上，RS 信号对于训练机器学习模型来说是嘈杂且不稳定的。组织样本的稀缺也使得学习用于临床用途的可靠深度学习网络变得具有挑战性。在本文中，我们提倡使用迁移对比学习范式（TCLP）来解决皮肤癌组织分类中 RS 的稀缺性和噪声特征。为了克服样本有限的挑战，TCLP 利用迁移学习，使用来自相似领域的 RS 数据（但从用于其他任务的不同 RS 设备收集）来预训练深度学习模型。为了解决 RS 信号的噪声性质，TCLP 使用对比学习来增强 RS 信号，以学习可靠的特征表示来表示 RS 信号以进行最终分类。实验和比较（包括统计测试）表明，对于基于 RS 信号的皮肤癌组织分类，TCLP 优于现有的深度学习基线。我们研究中使用的代码和数据均可在以下网址获取：https://github.com/yeyimilk/tclp。

Using Raman spectroscopy (RS) signals for skin cancer tissue classification has recently drawn significant attention, because of its non-invasive optical technique nature using molecular structures and conformations within biological tissue for diagnosis. In reality, RS signals are noisy and unstable for training machine learning models. The scarcity of tissue samples also makes it challenging to learn reliable deep-learning networks for clinical usages. In this paper, we advocate a Transfer Contrasting Learning Paradigm (TCLP) to address the scarcity and noisy characteristics of the RS for skin cancer tissue classification. To overcome the challenge of limited samples, TCLP leverages transfer learning to pre-train deep learning models using RS data from similar domains (but collected from different RS equipments for other tasks). To tackle the noisy nature of the RS signals, TCLP uses contrastive learning to augment RS signals to learn reliable feature representation to represent RS signals for final classification. Experiments and comparisons, including statistical tests, demonstrate that TCLP outperforms existing deep learning baselines for RS signal-based skin cancer tissue classification. Both code and data used in our study are available at: https://github.com/yeyimilk/tclp.