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首页> 外文会议>Conference on digital pathology >Automatic detection of invasive ductal carcinoma in whole slide images with Convolutional Neural Networks
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Automatic detection of invasive ductal carcinoma in whole slide images with Convolutional Neural Networks

机译：利用卷积神经网络自动检测整个幻灯片图像中的浸润性导管癌

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This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative results for automatic detection of IDC regions in WSI in terms of F-measure and balanced accuracy (71.80%, 84.23%), in comparison with an approach using handcrafted image features (color, texture and edges, nuclear textural and architecture), and a machine learning classifier for invasive tumor classification using a Random Forest. The best performing handcrafted features were fuzzy color histogram (67.53%, 78.74%) and RGB histogram (66.64%, 77.24%). Our results also suggest that at least some of the tissue classification mistakes (false positives and false negatives) were less due to any fundamental problems associated with the approach, than the inherent limitations in obtaining a very highly granular annotation of the diseased area of interest by an expert pathologist.
机译：本文提出了一种深度学习方法，用于对乳腺癌（BCa）的整个幻灯片图像（WSI）中的浸润性导管癌（IDC）组织区域进行自动检测和可视化分析。深度学习方法是一种从数据中学习的方法，涉及学习过程的计算模型。这种方法类似于人脑如何使用不同的解释级别或最具代表性和有用功能的层次来工作，从而形成层次化的学习表示形式。在语音识别和目标检测等几个领域，这些方法已被证明超越了最具挑战性问题的传统方法。浸润性乳腺癌的检测是一项耗时且具有挑战性的任务，主要是因为它涉及到病理学家扫描大范围的良性区域以最终确定恶性肿瘤的区域。 WSI中IDC的精确描绘对于随后评估肿瘤侵袭性分级和预测患者预后至关重要。 DL方法特别擅长处理此类问题，特别是如果有大量样本可用于训练时，这也将确保所学习特征和分类器的通用性。本文中的DL框架扩展了许多卷积神经网络（CNN），用于对肿瘤区域进行视觉语义分析以提供诊断支持。对CNN进行了来自WSI的大量图像块（组织区域）的训练，以学习基于层次的基于部分的表示。该方法是在WSI数据集上对162位被诊断为IDC的患者进行评估的。选择了113张幻灯片进行训练，并拿出49张幻灯片进行独立测试。通过数字化幻灯片上的专家病理学家对癌症区域的专家描述，为定量评估提供了依据。实验评估旨在测量WSI中检测IDC组织区域的分类器准确性。与使用手工图像特征（颜色，纹理和边缘，核纹理）的方法相比，我们的方法在F度量和平衡精度方面获得了自动检测WSI中IDC区域的最佳定量结果（71.80％，84.23％）和架构），以及使用随机森林对侵入性肿瘤进行分类的机器学习分类器。表现最佳的手工功能是模糊颜色直方图（67.53％，78.74％）和RGB直方图（66.64％，77.24％）。我们的研究结果还表明，至少有一些组织分类错误（假阳性和假阴性）的少归因于与该方法相关的任何基本问题，而不是固有的局限性，因为后者固有的局限性在于无法获得非常高粒度的目标病变区域注释。专家病理学家。

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