论文标题
RCNN用于整个幻灯片图像中感兴趣的区域检测
RCNN for Region of Interest Detection in Whole Slide Images
论文作者
论文摘要
近年来，数字病理学引起了极大的关注。整个幻灯片图像（WSIS）的分析是具有挑战性的，因为它们非常大，即GIGA像素分辨率。识别感兴趣的区域（ROI）是病理学家进一步分析癌症检测和其他异常诊断兴趣区域的第一步。在本文中，我们研究了RCNN的使用，RCNN是一种深度机器学习技术，用于仅使用少量标记的WSI进行训练来检测此类ROI。为了进行实验，我们使用了西澳大利亚州公共医院病理学服务的实际WSI。我们使用60个WSI来训练RCNN模型和另外12个WSI进行测试。该模型在新的看不见的WSI中进一步测试。结果表明，RCNN可有效地用于WSIS的ROI检测。
Digital pathology has attracted significant attention in recent years. Analysis of Whole Slide Images (WSIs) is challenging because they are very large, i.e., of Giga-pixel resolution. Identifying Regions of Interest (ROIs) is the first step for pathologists to analyse further the regions of diagnostic interest for cancer detection and other anomalies. In this paper, we investigate the use of RCNN, which is a deep machine learning technique, for detecting such ROIs only using a small number of labelled WSIs for training. For experimentation, we used real WSIs from a public hospital pathology service in Western Australia. We used 60 WSIs for training the RCNN model and another 12 WSIs for testing. The model was further tested on a new set of unseen WSIs. The results show that RCNN can be effectively used for ROI detection from WSIs.
