Johannes Kepler University Linz, Kepler University Hospital Linz, Department of Dermatology, Linz, Austria.
Johannes Kepler University Linz, Institute of Applied Physics, Department of Soft Matter Physics, Linz, Austria.
Mod Pathol. 2021 May;34(5):895-903. doi: 10.1038/s41379-020-00712-7. Epub 2020 Nov 13.
Recent advances in artificial intelligence, particularly in the field of deep learning, have enabled researchers to create compelling algorithms for medical image analysis. Histological slides of basal cell carcinomas (BCCs), the most frequent skin tumor, are accessed by pathologists on a daily basis and are therefore well suited for automated prescreening by neural networks for the identification of cancerous regions and swift tumor classification.In this proof-of-concept study, we implemented an accurate and intuitively interpretable artificial neural network (ANN) for the detection of BCCs in histological whole-slide images (WSIs). Furthermore, we identified and compared differences in the diagnostic histological features and recognition patterns relevant for machine learning algorithms vs. expert pathologists.An attention-ANN was trained with WSIs of BCCs to identify tumor regions (n = 820). The diagnosis-relevant regions used by the ANN were compared to regions of interest for pathologists, detected by eye-tracking techniques.This ANN accurately identified BCC tumor regions on images of histologic slides (area under the ROC curve: 0.993, 95% CI: 0.990-0.995; sensitivity: 0.965, 95% CI: 0.951-0.979; specificity: 0.910, 95% CI: 0.859-0.960). The ANN implicitly calculated a weight matrix, indicating the regions of a histological image that are important for the prediction of the network. Interestingly, compared to pathologists' eye-tracking results, machine learning algorithms rely on significantly different recognition patterns for tumor identification (p < 10).To conclude, we found on the example of BCC WSIs, that histopathological images can be efficiently and interpretably analyzed by state-of-the-art machine learning techniques. Neural networks and machine learning algorithms can potentially enhance diagnostic precision in digital pathology and uncover hitherto unused classification patterns.
近年来，人工智能领域，尤其是深度学习领域的进展，使得研究人员能够为医学图像分析创建引人注目的算法。皮肤科医生每天都会接触基底细胞癌（BCC）的组织学切片，这是最常见的皮肤肿瘤，因此非常适合神经网络进行自动预筛选，以识别癌性区域并快速进行肿瘤分类。在这项概念验证研究中，我们为组织学全切片图像（WSI）中 BCC 的检测实施了一种准确且直观可解释的人工神经网络（ANN）。此外，我们还确定并比较了与机器学习算法相关的诊断组织学特征和识别模式与专家病理学家之间的差异。我们使用 BCC 的 WSI 对注意力 ANN 进行了训练，以识别肿瘤区域（n=820）。ANN 使用的与诊断相关的区域与通过眼动追踪技术检测到的病理学家的感兴趣区域进行了比较。该 ANN 可以准确地识别组织学切片图像上的 BCC 肿瘤区域（ROC 曲线下面积：0.993，95%CI：0.990-0.995；敏感性：0.965，95%CI：0.951-0.979；特异性：0.910，95%CI：0.859-0.960）。ANN 隐式地计算了一个权重矩阵，该矩阵指示对网络预测重要的组织学图像区域。有趣的是，与病理学家的眼动追踪结果相比，机器学习算法在识别肿瘤时依赖于明显不同的识别模式（p<10）。总之，我们在 BCC WSI 的示例中发现，最先进的机器学习技术可以有效地分析组织病理学图像，并解释其结果。神经网络和机器学习算法有可能提高数字病理学中的诊断精度，并揭示迄今未使用的分类模式。
