论文标题
用导管实例的管道对乳房组织病理学图像进行分类
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline
论文作者
论文摘要
在这项研究中，我们提出了导管实例的管道（DIOP），其中包含导管级实例分割模型，组织级的语义分割模型以及用于诊断分类的三级特征。基于实例分割和蒙版R-CNN模型的最新进展，我们的管道级分段器试图在微观图像中识别每个导管个体。然后，它从确定的导管实例中提取组织级信息。利用从这些导管实例获得的三个级别的信息以及组织病理学的图像，在所有诊断任务中，提出的DIOP优于先前的方法（既优于基于功能的基于功能和CNN）；对于四向分类任务，DIOP在此独特的数据集中获得了与普通病理学家的可比性。提出的DIOP在推理时间仅需几秒钟即可运行，这可以在大多数现代计算机上进行交互使用。需要更多的临床探索来研究未来该系统的鲁棒性和普遍性。
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask R-CNN model, our duct-level segmenter tries to identify each ductal individual inside a microscopic image; then, it extracts tissue-level information from the identified ductal instances. Leveraging three levels of information obtained from these ductal instances and also the histopathology image, the proposed DIOP outperforms previous approaches (both feature-based and CNN-based) in all diagnostic tasks; for the four-way classification task, the DIOP achieves comparable performance to general pathologists in this unique dataset. The proposed DIOP only takes a few seconds to run in the inference time, which could be used interactively on most modern computers. More clinical explorations are needed to study the robustness and generalizability of this system in the future.
