使用深度与集成机器学习提升结直肠癌组织学分解的性能
Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning
AI总结：
本研究提出一种混合深度与集成机器学习模型，用于从全切片图像中准确分类结直肠癌组织类型，在内外部测试集上分别达到99.89%和96.74%的准确率，有效提升了基于成像的生物标志物的预后潜力。
摘要：
在常规结直肠癌管理中，苏木精和伊红染色的组织学样本被普遍使用。尽管如此，它们在定义用于患者分层和治疗选择的客观生物标志物方面的潜力仍在探索中。目前的金标准依赖于昂贵且耗时的基因检测。然而，最近的研究强调了卷积神经网络（CNN）在从这些易于获取的图像中提取临床相关生物标志物方面的潜力。这些基于CNN的生物标志物可以与金标准相媲美地预测患者结果，并具有速度快、自动化和成本低的额外优势。基于CNN的生物标志物的预测潜力从根本上依赖于卷积神经网络（CNN）从全切片显微镜图像中准确分类不同组织类型的能力。因此，提高组织类别分解的准确性对于放大基于成像的生物标志物的预后潜力至关重要。本研究引入了一种混合深度与集成机器学习模型，该模型超越了此前针对该分类任务的所有解决方案。我们的模型在外部测试集上达到了96.74%的准确率，在内部测试集上达到了99.89%的准确率。认识到这些模型在推进该任务方面的潜力，我们已将其公开以供进一步研究和开发。
英文摘要：
In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) in facilitating the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of convolutional neural networks (CNNs) to classify diverse tissue types from whole slide microscope images accurately. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid Deep and ensemble machine learning model that surpassed all preceding solutions for this classification task. Our model achieved 96.74% accuracy on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in advancing the task, we have made them publicly available for further research and development.
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