ArticleScientific reports2021
Deep learning data augmentation for Raman spectroscopy cancer tissue classification.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of AI-based raman spectroscopy in the diagnosis of gastric cancer: a systematic review and meta-analysis.Lasers in medical science · 2026Pooled it
- AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.Biosensors · 2026Review
- Training and validation of a 1D CNN model for accurate three-class classification of oral cancer using serum Raman spectroscopy.Discover oncology · 2026Article
- Machine Learning-Enabled Intelligent Analysis of Surface-Enhanced Raman Scattering: Methods, Applications, and Perspectives.Molecules (Basel, Switzerland) · 2026Review
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.Lasers in medical science · 2026Review
- Domain-Level Classification of Archaea and Bacteria Using AI-Assisted Single-Cell Raman Spectroscopy.ACS omega · 2026Article
- Developing an Integrated Toolbox for Raman Spectral Analysis with Both Artificial Neural Networks and Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2026Article
- Towards Rapid Calibration of Bioprocess Quantification Models Using Single Compound Raman Spectra: A Comparison of Four Approaches.Biotechnology and bioengineering · 2026Article
- Artificial intelligence guided Raman spectroscopy in biomedicine: Applications and prospects.Journal of pharmaceutical analysis · 2025Review
- A Setup for Automatic Raman Measurements in High-Throughput Experimentation.Biotechnology and bioengineering · 2025Article
- Machine-Learning-Aided Advanced Electrochemical Biosensors.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Exploring Generative Artificial Intelligence and Data Augmentation Techniques for Spectroscopy Analysis.Chemical reviews · 2025Review
- AI-assisted identification of nonmelanoma skin cancer structures based on combined line-field confocal optical coherence tomography and confocal Raman microspectroscopy.Journal of biomedical optics · 2025Article
- Raman Spectroscopy in Cellular and Tissue Aging Research.Aging cell · 2025Review
- Article
- Random splicing assisted deep learning for breast cancer cell line classification via Raman spectroscopy.Computational and structural biotechnology journal · 2025Article
- Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections.Communications biology · 2024Article
- Intraoperative detection of parathyroid glands using artificial intelligence: optimizing medical image training with data augmentation methods.Surgical endoscopy · 2024Article
- Imbalanced spectral data analysis using data augmentation based on the generative adversarial network.Scientific reports · 2024Article
- Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy.PNAS nexus · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.