Label free identification of different cancer cells using deep learning-based image analysis
Label free identification of different cancer cells using deep learning-based image analysis
复制标题
使用基于深度学习的图像分析对不同癌细胞进行无标记识别
DOI：
10.1063/5.0141730
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Karl Gardner;Rutwik Joshi;Md Nayeem Hasan Kashem;T. Pham;Qiugang Lu;Wei Li
中科院分区：
文献类型：
--
作者：
Karl Gardner;Rutwik Joshi;Md Nayeem Hasan Kashem;T. Pham;Qiugang Lu;Wei Li
癌症诊断是癌症恢复和生存的一个重要领域，需要许多昂贵的程序来实施正确的治疗。机器学习(ML)方法可以帮助液体活检中的循环肿瘤细胞或固体活检中的原发肿瘤的诊断预测。在从深度学习模型预测转移潜力后，临床环境中的医生可以对特定患者进行安全和正确的治疗。本文研究了使用深度卷积神经网络来预测特定的癌细胞系作为一种无标记鉴定的工具。具体地说，描述了权重初始化和性能度量的深度学习策略，并将迁移学习和准确度度量用于本工作。用于预测的设备包括明场显微镜，而不使用化学标记、先进的仪器或耗时的生物技术，这使其比目前的诊断方法更具优势。在这个过程中，收集了三个不同的已知癌细胞株的二进制数据集，每个数据集的转移潜力都不同。采用了两种不同的分类模型(EfficientNetV2和ResNet-50)，并对ML体系结构中的每个阶段进行了分析。给出了每个模型和数据集的训练结果，并对其进行了系统比较。我们发现，两个ML模型的测试集准确率都表现出了良好的性能，其中EfficientNetV2的准确率达到了99%。这些测试结果使EfficientNetV2的表现优于ResNet-50，每个数据集的平均增长率为3.5%。从预测中获得的高精度表明，该系统可以在大规模临床数据集上进行再训练。
Cancer diagnostics is an important field of cancer recovery and survival with many expensive procedures needed to administer the correct treatment. Machine Learning (ML) approaches can help with the diagnostic prediction from circulating tumor cells in liquid biopsy or from a primary tumor in solid biopsy. After predicting the metastatic potential from a deep learning model, doctors in a clinical setting can administer a safe and correct treatment for a specific patient. This paper investigates the use of deep convolutional neural networks for predicting a specific cancer cell line as a tool for label free identification. Specifically, deep learning strategies for weight initialization and performance metrics are described, with transfer learning and the accuracy metric utilized in this work. The equipment used for prediction involves brightfield microscopy without the use of chemical labels, advanced instruments, or time-consuming biological techniques, giving an advantage over current diagnostic methods. In the procedure, three different binary datasets of well-known cancer cell lines were collected, each having a difference in metastatic potential. Two different classification models were adopted (EfficientNetV2 and ResNet-50) with the analysis given for each stage in the ML architecture. The training results for each model and dataset are provided and systematically compared. We found that the test set accuracy showed favorable performance for both ML models with EfficientNetV2 accuracy reaching up to 99%. These test results allowed EfficientNetV2 to outperform ResNet-50 at an average percent increase of 3.5% for each dataset. The high accuracy obtained from the predictions demonstrates that the system can be retrained on a large-scale clinical dataset.