Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features
- Center for Public Health Genomics（公共卫生基因组学中心）
- Department of Biomedical Engineering University of Virginia（弗吉尼亚大学生物医学工程系）
- University of Virginia（弗吉尼亚大学）
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英文摘要：
White Blood Cell (WBC) Leukaemia is detected through image-based classification. Convolutional Neural Networks are used to learn the features needed to classify images of cells a malignant or healthy. However, this type of model requires learning a large number of parameters and is difficult to interpret and explain. Explainable AI (XAI) attempts to alleviate this issue by providing insights to how models make decisions. Therefore, we present an XAI model which uses only 24 explainable and interpretable features and is highly competitive to other approaches by outperforming them by about 4.38\%. Further, our approach provides insight into which variables are the most important for the classification of the cells. This insight provides evidence that when labs treat the WBCs differently, the importance of various metrics changes substantially. Understanding the important features for classification is vital in medical imaging diagnosis and, by extension, understanding the AI models built in scientific pursuits.
