Pulido J Vince, Guleria Shan, Ehsan Lubaina, Shah Tilak, Syed Sana, Brown Don E
Johns Hopkins University, Applied Physics Lab, Laurel, MD.
University of Virginia, School of Medicine, Charlottesville, VA.
Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1659-1663. doi: 10.1109/isbi45749.2020.9098630. Epub 2020 May 22.
Histologic diagnosis of Barrett's esophagus and esophageal malignancy via probe-based confocal laser endomicroscopy (pCLE) allows for real-time examination of epithelial architecture and targeted biopsy sampling. Although pCLE demonstrates high specificity, sensitivity remains low. This study employs deep learning architectures in order to improve the accuracy of pCLE in diagnosing esophageal cancer and its precursors. pCLE videos are curated and annotated as belonging to one of the three classes: squamous, Barrett's (intestinal metaplasia without dysplasia), or dysplasia. We introduce two novel video architectures, AttentionPooling and Multi-Module AttentionPooling deep networks, that outperform other models and demonstrate a high degree of explainability.
通过基于探头的共聚焦激光内镜检查(pCLE)对巴雷特食管和食管恶性肿瘤进行组织学诊断,可实现对上皮结构的实时检查和靶向活检采样。尽管pCLE显示出高特异性,但其敏感性仍然较低。本研究采用深度学习架构,以提高pCLE诊断食管癌及其癌前病变的准确性。pCLE视频被整理并标注为属于三个类别之一:鳞状上皮、巴雷特食管(无异型增生的肠化生)或异型增生。我们引入了两种新颖的视频架构,即注意力池化和多模块注意力池化深度网络,它们优于其他模型,并具有高度的可解释性。