Background:Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for applications in oral cancer diagnosis. As a non-traditional imaging modality, the autofluorescence signal collected from the system cannot be directly visually assessed by a clinician and a model is needed to generate a diagnosis for each image. However, training a deep learning model from scratch on small multispectral autofluorescence datasets can fail due to inter-patient variability, poor initialization, and overfitting.Methods:We propose a contrastive-based pre-training approach that teaches the network to perform patient normalization without requiring a direct comparison to a reference sample. We then use the contrastive pre-trained encoder as a favorable initialization for classification. To train the classifiers, we efficiently use available data and reduce overfitting through a multitask framework with margin delineation and cancer diagnosis tasks. We evaluate the model over 67 patients using 10-fold cross-validation and evaluate significance using paired, one-tailedt-tests.Results:The proposed approach achieves a sensitivity of 82.08% and specificity of 75.92% on the cancer diagnosis task with a sensitivity of 91.83% and specificity of 79.31% for margin delineation as an auxiliary task. In comparison to existing approaches, our method significantly outperforms asupport vector machine(SVM) implemented with eithersequential feature selection(SFS) (p= 0.0261) or L1 loss (p= 0.0452) when considering the average of sensitivity and specificity. Specifically, the proposed approach increases performance by 2.75% compared to the L1 model and 4.87% compared to the SFS model. In addition, there is a significant increase in specificity of 8.34% compared to the baseline autoencoder model (p= 0.0070).Conclusions:Our method effectively trains deep learning models for small data applications when existing, large pre-trained models are not suitable for fine-tuning. While we designed the network for a specific imaging modality, we report the development process so that the insights gained can be applied to address similar challenges in other non-traditional imaging modalities. A key contribution of this paper is a neural network framework for multi-spectral fluorescence lifetime-based tissue discrimination that performs patient normalization without requiring a reference (healthy) sample from each patient at test time.
背景：近年来，多光谱自体荧光寿命成像系统被开发用于快速、无创地评估组织特性，以应用于口腔癌诊断。作为一种非传统成像模式，该系统采集的自体荧光信号无法由临床医生直接进行视觉评估，因此需要模型对每幅图像生成诊断结果。然而，在小型多光谱自体荧光数据集上从头训练深度学习模型可能因患者间差异、初始化不良和过拟合等问题而失败。 方法：我们提出一种基于对比学习的预训练方法，该方法无需与参考样本直接比较即可使网络实现患者归一化。随后将对比预训练的编码器作为分类任务的优化初始化参数。在分类器训练中，我们通过结合边缘勾画与癌症诊断任务的多任务框架高效利用可用数据并降低过拟合风险。采用10折交叉验证对67例患者数据进行模型评估，并通过配对单尾t检验评估显著性。 结果：所提方法在癌症诊断任务中达到82.08%的敏感度和75.92%的特异度，作为辅助任务的边缘勾画任务则获得91.83%的敏感度和79.31%的特异度。与现有方法相比，当综合考虑敏感度与特异度均值时，本方法显著优于采用序列特征选择（p=0.0261）或L1损失函数（p=0.0452）的支持向量机模型。具体而言，相较于L1模型性能提升2.75%，较SFS模型提升4.87%。与基线自编码器模型相比，特异度显著提高8.34%（p=0.0070）。 结论：当现有大型预训练模型不适用于微调场景时，本方法能有效训练适用于小数据应用的深度学习模型。虽然网络设计针对特定成像模式，但我们详细报告了开发过程，所得洞见可推广至其他非传统成像模式的类似挑战。本文的核心贡献在于提出一种基于多光谱荧光寿命的组织鉴别神经网络框架，该框架在测试时无需获取每位患者的参考（健康）样本即可实现患者归一化处理。
肿瘤(癌症)患者之家