Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features
This work addresses the need for interpretable models in medical imaging diagnosis, though it appears incremental as it builds on existing explainable AI methods.
The paper tackled the problem of classifying white blood cell leukemia images by developing an explainable AI model that uses only 24 interpretable features, achieving a 4.38% improvement over other approaches.
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.