论文标题
白细胞白血病的分类很少
Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features
论文作者
论文摘要
通过基于图像的分类检测到白细胞（WBC）白血病。卷积神经网络用于学习分类细胞图像恶性或健康所需的特征。但是，这种类型的模型需要学习大量参数，并且难以解释和解释。可解释的AI（XAI）试图通过提供模型如何做出决定的见解来减轻此问题。因此，我们提出了一个仅使用24个可解释和可解释的特征的XAI模型，并且通过优于4.38 \％的方法，与其他方法具有高度竞争力。此外，我们的方法提供了有关哪些变量对于细胞分类最重要的见解。该见解提供了证据表明，当实验室对WBC的处理方式不同时，各种指标的重要性会发生很大变化。了解分类的重要特征在医学成像诊断中至关重要，并且通过扩展了解了基于科学追求的AI模型。
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.
