A decision support system for acute lymphoblastic leukemia detection based on explainable artificial intelligence

The detection of acute lymphoblastic leukemia (ALL) via deep learning (DL) has received great interest because of its high accuracy in detecting lymphoblasts without the need for handcrafted feature extraction. However, current DL models, such as convolutional neural networks and vision Transformers, are extremely complex, making them black boxes that perform classification in an obscure way. To compensate for this and increase the explainability of the decisions made by such methods, in this paper, we propose an innovative decision support system for ALL detection that is based on DL and explainable artificial intelligence (XAI). Our approach first introduces causality into the decision with a metric learning approach, enabling a decision to be made by analyzing the most similar images in the database. Second, our method integrates XAI techniques to allow even non-trained personnel to obtain an informed decision by analyzing which regions of the images are most similar and how the samples are organized in the latent space. The results on publicly available ALL databases confirm the validity of our approach in opening the black box while achieving similar or superior accuracy to that of existing approaches. • This paper presents an innovative and explainable decision support system for detecting acute lymphoblastic (or lymphocytic) leukemia (ALL). • The approach introduces causability in the decision by using metric learning and majority voting. • The approach integrates XAI techniques based on CAM and dimensionality reduction to support the decision. • The paper presents results on several publicly available ALL databases. • This contribution could pave the way for a greater acceptance of CAD systems based on deep learning.