Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images.
Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images.
复制标题
多标签的多标签学习，用于整个滑梯乳房组织病理学图像的多类分类。
DOI：
10.1109/tmi.2017.2758580
复制
发表时间：
2018-01
影响因子：
10.6
通讯作者：
Elmore JG
中科院分区：
文献类型：
--
作者：
Mercan C;Aksoy S;Mercan E;Shapiro LG;Weaver DL;Elmore JG
数字病理学已经进入了一个新的时代，全载玻片扫描仪的可用性，创建高分辨率的全活检载玻片图像。因此，关于图像区域与病理学家在载玻片水平上分配的诊断标签之间的对应关系的不确定性，以及识别属于具有不同临床意义的多个类别的区域的需要已经成为两个新的挑战。然而，目前尚不清楚最先进的算法对这些多类学习和定位问题的可推广性，这些算法的准确性是在仔细选择的二进制良性与癌症分类的感兴趣区域（ROI）上报告的。本文提出了我们的潜在解决方案，这些挑战，利用查看记录的病理学家和他们的幻灯片级注释在弱监督学习的情况下。首先，我们根据不同的行为，如缩放，平移和固定，从病理学家的图像筛选日志中提取候选ROI。然后，我们用候选ROI表示的一组实例和从病理形式中提取的一组类标签对每张幻灯片进行建模。最后，我们使用四种不同的多实例多标签学习算法，用于在整个幻灯片乳腺组织病理学图像中对诊断类别进行幻灯片级和ROI级预测。使用5类和14类设置的幻灯片级评估显示，在不同的弱标记学习场景下，平均精度值分别高达81%和69%。ROI水平的预测表明，分类器可以成功地执行多类定位和分类的整个幻灯片图像，选择包括全方位的具有挑战性的诊断类别。
Digital pathology has entered a new era with the availability of whole slide scanners that create high-resolution images of full biopsy slides. Consequently, the uncertainty regarding the correspondence between the image areas and the diagnostic labels assigned by pathologists at the slide level, and the need for identifying regions that belong to multiple classes with different clinical significance have emerged as two new challenges. However, generalizability of the state-of-the-art algorithms, whose accuracies were reported on carefully selected regions of interest (ROI) for the binary benign versus cancer classification, to these multi-class learning and localization problems is currently unknown. This paper presents our potential solutions to these challenges by exploiting the viewing records of pathologists and their slide-level annotations in weakly supervised learning scenarios. First, we extract candidate ROIs from the logs of pathologists’ image screenings based on different behaviors, such as zooming, panning, and fixation. Then, we model each slide with a bag of instances represented by the candidate ROIs and a set of class labels extracted from the pathology forms. Finally, we use four different multi-instance multi-label learning algorithms for both slide-level and ROI-level predictions of diagnostic categories in whole slide breast histopathology images. Slide-level evaluation using 5-class and 14-class settings showed average precision values up to 81% and 69%, respectively, under different weakly-labeled learning scenarios. ROI-level predictions showed that the classifier could successfully perform multi-class localization and classification within whole slide images that were selected to include the full range of challenging diagnostic categories.