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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks
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Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks

机译：深度卷积网络的整个载玻片乳腺组织病理学图像中癌症的检测和分类

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摘要

Generalizability of algorithms for binary cancer vs. no cancer classification is unknown for clinically more significant multi-class scenarios where intermediate categories have different risk factors and treatment strategies. We present a system that classifies whole slide images (WSI) of breast biopsies into five diagnostic categories. First, a saliency detector that uses a pipeline of four fully convolutional networks, trained with samples from records of pathologists' screenings, performs multi-scale localization of diagnostically relevant regions of interest in WSI. Then, a convolutional network, trained from consensus derived reference samples, classifies image patches as non-proliferative or proliferative changes, atypical ductal hyperplasia, ductal carcinoma in situ, and invasive carcinoma. Finally, the saliency and classification maps are fused for pixel-wise labeling and slide-level categorization. Experiments using 240 WSI showed that both saliency detector and classifier networks performed better than competing algorithms, and the five-class slide-level accuracy of 55% was not statistically different from the predictions of 45 pathologists. We also present example visualizations of the learned representations for breast cancer diagnosis. (C) 2018 Elsevier Ltd. All rights reserved.
机译：二元癌算法的概括性与癌症分类未知癌症分类是未知的中间类别具有不同风险因素和治疗策略的临床更重要的多级情景。我们展示了一个系统，将乳房活检的整个幻灯片图像（WSI）分为五个诊断类别。首先，使用四个完全卷积网络的管道的显着探测器，其中包含来自病理学家筛查记录的样本，对WSI的诊断相关的兴趣区域进行了多尺度定位。然后，从共有衍生的参考样品培训的卷积网络，将图像斑块分类为不增殖或增殖性变化，非典型导管增生，导管原位和侵入性癌。最后，显着性和分类映射被融合用于像素 - 方向标签和幻灯片级分类。使用240 WSI的实验表明，显着探测器和分类器网络比竞争算法更好，55％的幻灯片级精度与45个病理学家的预测没有统计学不同。我们还提出了乳腺癌诊断所学习言论的示例可视化。 （c）2018年elestvier有限公司保留所有权利。

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