Gecer Baris, Aksoy Selim, Mercan Ezgi, Shapiro Linda G, Weaver Donald L, Elmore Joann G
Department of Computer Engineering, Bilkent University, Ankara, 06800, Turkey.
Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA.
Pattern Recognit. 2018 Dec;84:345-356. doi: 10.1016/j.patcog.2018.07.022. Epub 2018 Jul 20.
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.
对于临床上更具意义的多类别情况，即中间类别具有不同风险因素和治疗策略的情况，二元癌症与非癌症分类算法的可推广性尚不清楚。我们提出了一种将乳腺活检的全切片图像（WSI）分类为五个诊断类别的系统。首先，一个显著性检测器使用由四个全卷积网络组成的管道，通过病理学家筛查记录中的样本进行训练，对WSI中诊断相关的感兴趣区域进行多尺度定位。然后，一个从共识衍生的参考样本训练的卷积网络，将图像块分类为非增殖性或增殖性变化、非典型导管增生、导管原位癌和浸润性癌。最后，将显著性和分类图融合以进行逐像素标记和玻片级分类。使用240张WSI进行的实验表明，显著性检测器和分类器网络的表现均优于竞争算法，55%的五类玻片级准确率与45位病理学家的预测在统计学上没有差异。我们还展示了用于乳腺癌诊断的学习表示的示例可视化。
