Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading
Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading
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DOI：
10.3389/fbioe.2019.00145
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发表时间：
2019-06-21
影响因子：
5.7
通讯作者：
Racoceanu, Daniel
中科院分区：
文献类型：
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作者：
Jimenez, Gabriel;Racoceanu, Daniel
现有的计算方法还没有产生用于病理学家日常实践的有效和高效的计算机辅助工具。本研究专注于基于计算机的乳腺癌诊断资格，提出了两种深度学习架构，以有效地检测和分类组织病理学组织样本中的有丝分裂。第一种方法由两部分组成，需要对数字组织学图像进行预处理，并使用自由手工制作的特征卷积神经网络（CNN）进行二进制分类。结果表明，该方法可以达到95%的准确率在测试中，与F1得分为94.35%。这一结果高于使用经典图像处理技术的结果，也高于将CCN与手工特征相结合的方法。第二种方法是使用语义分割的端到端方法。结果表明，该算法在测试中可以达到95%以上的准确率，平均Dice指数为0.6，高于现有的CNN结果（0.9 F1-score）。此外，由于深度学习方法的语义属性，端到端深度学习框架可以执行两项任务：有丝分裂的检测和分类。结果显示了深度学习在分析全载玻片图像（WSI）及其集成到计算机辅助系统中的潜力。最后几节还讨论了将这项工作扩展到整个幻灯片图像;以及在构建受所提出技术启发的计算机辅助系统时有用的一些计算要点。
Existing computational approaches have not yet resulted in effective and efficient computer-aided tools that are used in pathologists' daily practice. Focusing on a computer-based qualification for breast cancer diagnosis, the present study proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consists of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing, with an F1-score of 94.35%. This result is higher than the results using classical image processing techniques and also higher than the approaches combining CCNs with handcrafted features. The second approach is an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6, higher than the existing results using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results show the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last sections; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the proposed technology.