University College Dublin, CeADAR: Centre for Applied Data Analytics Research, School of Computer Science, Dublin, Ireland.
Universitat de Girona, Institute of Computer Vision and Robotics, Girona, Spain.
Comput Methods Programs Biomed. 2018 Oct;165:69-76. doi: 10.1016/j.cmpb.2018.08.006. Epub 2018 Aug 16.
Automatic classification of healthy tissues and organs based on histology images is an open problem, mainly due to the lack of automated tools. Solutions in this regard have potential in educational medicine and medical practices. Some preliminary advances have been made using image processing techniques and classical supervised learning. Due to the breakthrough performance of deep learning in various areas, we present an approach to recognise and classify, automatically, fundamental tissues and organs using Convolutional Neural Networks (CNN).
We adapt four popular CNNs architectures - ResNet, VGG19, VGG16 and Inception - to this problem through transfer learning. The resulting models are evaluated at three stages. Firstly, all the transferred networks are compared to each other. Secondly, the best resulting fine-tuned model is compared to an ad-hoc 2D multi-path model to outline the importance of transfer learning. Thirdly, the same model is evaluated against the state-of-the-art method, a cascade SVM using LBP-based descriptors, to contrast a traditional machine learning approach and a representation learning one. The evaluation task consists of separating six classes accurately: smooth muscle of the elastic artery, smooth muscle of the large vein, smooth muscle of the muscular artery, cardiac muscle, loose connective tissue, and light regions. The different networks are tuned on 6000 blocks of 100 × 100 pixels and tested on 7500.
Our proposal yields F-score values between 0.717 and 0.928. The highest and lowest performances are for cardiac muscle and smooth muscle of the large vein, respectively. The main issue leading to limited classification scores for the latter class is its similarity with the elastic artery. However, this confusion is evidenced during manual annotation as well. Our algorithm reached improvements in F-score between 0.080 and 0.220 compared to the state-of-the-art machine learning approach.
We conclude that it is possible to classify healthy cardiovascular tissues and organs automatically using CNNs and that deep learning holds great promise to improve tissue and organs classification. We left our training and test sets, models and source code publicly available to the research community.
基于组织学图像对健康组织和器官进行自动分类是一个悬而未决的问题，主要是因为缺乏自动化工具。这方面的解决方案在医学教育和医学实践中有很大的应用潜力。一些初步的进展已经使用图像处理技术和传统的监督学习方法取得。由于深度学习在各个领域的突破性表现，我们提出了一种使用卷积神经网络（CNN）自动识别和分类基本组织和器官的方法。
我们通过迁移学习将四种流行的 CNN 架构 - ResNet、VGG19、VGG16 和 Inception - 应用于这个问题。在三个阶段评估得到的模型。首先，比较所有转移网络之间的差异。其次，比较最佳的微调模型与专门的二维多路径模型，以突出迁移学习的重要性。第三，将同一模型与基于 LBP 描述符的级联 SVM 的最新方法进行比较，以对比传统的机器学习方法和表示学习方法。评估任务是准确区分六个类别：弹性动脉的平滑肌、大静脉的平滑肌、肌性动脉的平滑肌、心肌、疏松结缔组织和浅色区域。不同的网络在 6000 个 100×100 像素的块上进行调整，并在 7500 个块上进行测试。
我们的方法在 F-score 值上达到了 0.717 到 0.928 之间。性能最高和最低的分别是心肌和大静脉的平滑肌。导致后者分类得分有限的主要问题是其与弹性动脉的相似性。然而，这种混淆在手动注释中也得到了证实。与最先进的机器学习方法相比，我们的算法在 F-score 上提高了 0.080 到 0.220 之间。
我们得出结论，使用 CNN 对健康的心血管组织和器官进行自动分类是可行的，并且深度学习在提高组织和器官分类方面具有很大的潜力。我们将训练和测试集、模型和源代码公开提供给研究社区。
