Self-explaining Artificial Intelligence for the Classification of B cell Non-Hodgkin Lymphoma

Artificial intelligence (AI) systems have been proposed for multiparameter flow cytometry (MFC) based immunophenotyping of leukemic Non-Hodgkin-Lymphoma (NHL). Lymphoma classification has progressively been revised due to the increasing molecular knowledge. In order to establish a ground truth for AI learning we sought to disclose the data’s structure embodiment in comparison to the histopathological classification decisions through self-organization of data using swarm intelligence. 19,493 data samples allowed for an unsupervised view based on multidimensional MFC data hereby providing information about the higher structures within the data and the inherent discrimination capabilities. Yet, rare lymphoma entities remain a particular challenge for AI training. We propose an AI system termed Flow XAI, which exhibits equal immunophenotyping performance as neural network based systems but has reduced the number of needed learning data by a factor of 100. The Flow XAI is capable of “self-reflection”, it reports a self-competence estimation for each case. Moreover, it selects and reports diagnostically relevant cell populations and expression patterns in a discernable and clear manner so that physicians can understand the rationale behind the AI’s decisions. The self-explaining AI system can therefore be used for real-world training on MCF based lymphoma immunophenotyping.