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首页> 外文会议>IEEE International Symposium on Biomedical Imaging >LEARNING BASAL CELL CARCINOMA PATTERNS BY FUSING PATHOLOGISTS' WSI NAVIGATIONS AND GRAPH-BASED CENTRALITY FEATURES
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LEARNING BASAL CELL CARCINOMA PATTERNS BY FUSING PATHOLOGISTS' WSI NAVIGATIONS AND GRAPH-BASED CENTRALITY FEATURES

机译：通过融合病理学家的WSI导航和基于图形的中心特征来学习基础细胞癌模式

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摘要

This article introduces a model that automatically detects suspicious basal cell carcinoma regions in whole slide images (WSI) by integrating nuclear architectural features and information captured from WSI pathologists' navigations during diagnostic tasks. In such an approach, manual annotations are not needed since high-level expert knowledge is implicitly captured when the pathologist is exploring the WSI. The method was tested on a set of 10 cases of patients diagnosed with basal cell carcinoma using a leave-one-out cross-validation technique. At each iteration, a quadratic discriminant analysis classifier was trained to identify cancerous nuclei using architectural features of nuclei belonging to the regions that were highly or little visited by pathologists when rendering a diagnosis. Experimental results showed an average accuracy of 86% and an F-score of 76%, thereby demonstrating the potential of this approach to be included in actual clinical scenarios.
机译：本文介绍一种模型，通过将核架构特征和从WSI病理学家在诊断任务期间的导航期间集成来自动检测整个幻灯片图像（WSI）中的可疑基础细胞癌区域。在这种方法中，由于当病理学家探索WSI时，毫无含蓄地捕获了高级专家知识，因此不需要手动注释。使用休假交叉验证技术在一组10例患者上测试了该方法。在每次迭代时，培训二次判别分析分类剂以培训使用属于病理学家在诊断时高度或很少地访问的区域的核的建筑特征来识别癌细胞。实验结果表明，平均精度为86％，F分数为76％，从而展示了这种方法的潜力在实际的临床情景中。

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