AUTOMATED COLITIS DETECTION FROM ENDOSCOPIC BIOPSIES AS A TISSUE SCREENING TOOL IN DIAGNOSTIC PATHOLOGY.
AUTOMATED COLITIS DETECTION FROM ENDOSCOPIC BIOPSIES AS A TISSUE SCREENING TOOL IN DIAGNOSTIC PATHOLOGY.
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内窥镜活检中的自动结肠炎检测作为诊断病理学中的组织筛查工具。
DOI：
10.1109/icip.2012.6467483
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Kovačević,Jelena
中科院分区：
文献类型：
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作者：
McCann,MichaelT;Bhagavatula,Ramamurthy;Fickus,MatthewC;Ozolek,JohnA;Kovačević,Jelena
我们提出了一种方法，用于识别结肠活检中的结肠炎，作为我们的框架的扩展，用于自动识别组织学图像中的组织。组织学是临床和研究应用中的关键工具，但即使是普通的组织学分析，如结肠活检的筛选，也必须由训练有素的病理学家以每小时高成本进行，这表明潜在的自动化利基市场。为此，我们建立在我们以前的工作，通过扩展组织病理学词汇（一组基于病理学家使用的视觉线索的功能）与结肠炎应用程序驱动的新功能。我们使用多实例学习框架来允许我们的像素级分类器从图像级训练标签中学习。新系统实现了与最先进的生物图像分类器相媲美的精度，具有更少，更直观的功能。
We present a method for identifying colitis in colon biopsies as an extension of our framework for the automated identification of tissues in histology images. Histology is a critical tool in both clinical and research applications, yet even mundane histological analysis, such as the screening of colon biopsies, must be carried out by highly-trained pathologists at a high cost per hour, indicating a niche for potential automation. To this end, we build upon our previous work by extending the histopathology vocabulary (a set of features based on visual cues used by pathologists) with new features driven by the colitis application. We use the multiple-instance learning framework to allow our pixel-level classifier to learn from image-level training labels. The new system achieves accuracy comparable to state-of-the-art biological image classifiers with fewer and more intuitive features.