Transfer Contrastive Learning for Raman Spectroscopy Skin Cancer Tissue Classification

Area of Science:

• Biomedical Optics
• Medical Diagnostics
• Machine Learning in Healthcare

Background:

• Raman spectroscopy (RS) is a promising non-invasive optical technique for skin cancer tissue classification, analyzing molecular structures.
• Challenges in clinical application include noisy and unstable RS signals and a scarcity of tissue samples, hindering reliable deep learning model training.

Purpose of the Study:

• To introduce a novel Transfer Contrasting Learning Paradigm (TCLP) for skin cancer tissue classification using Raman spectroscopy signals.
• To address the limitations of data scarcity and signal noise inherent in RS data for machine learning models.

Main Methods:

• TCLP utilizes transfer learning to pre-train deep learning models with RS data from similar domains, mitigating the limited sample size issue.
• Contrastive learning is employed within TCLP to augment RS signals, learning robust feature representations and overcoming signal noise.
• The proposed method was evaluated against existing deep learning baselines using experiments and statistical tests.

Main Results:

• The Transfer Contrasting Learning Paradigm (TCLP) demonstrated superior performance compared to current deep learning methods for skin cancer tissue classification based on RS signals.
• The approach effectively handles noisy RS signals and limited sample availability, crucial for clinical translation.
• Statistical tests confirmed the significant outperformance of TCLP over baseline models.

Conclusions:

• TCLP offers a robust solution for skin cancer classification using Raman spectroscopy, effectively managing data scarcity and signal noise.
• The paradigm shows significant potential for improving the reliability and clinical applicability of deep learning models in dermatological diagnostics.
• This work advances the use of optical techniques and machine learning in non-invasive cancer detection.