Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study

13 Jul 20264 min read0 viewsJournal Feed

GIST (Key Takeaways)

by Michael C. Thrun, Jörg Hoffmann, Stefan W.
Krause, Peter Krawitz, Quirin Stier, Andreas Neubauer, Cornelia Brendel, Alfred Ultsch Background Multiparameter flow cytometry is a cornerstone of B cell non-Hodgkin lymphoma (B-NHL) diagnostics, but interpretation requires substantial expertise and is complicated by high-dimensional data, variable sample quality, limited data for rare entities, and evolving clinical classification systems. Current artificial intelligence approaches often require large training datasets and provide limited insight into the rationale behind individual diagnostic decisions.
Methods and findings We developed FlowXAI, a self-explaining artificial intelligence system designed to support B-NHL classification while explicitly reporting case-level diagnostic trustworthiness. FlowXAI combines unsupervised structural analysis with a clinically motivated, multi-level diagnostic framework reflecting routine diagnostic priorities.
An unsupervised Tile Mining (TM) procedure performs pre-diagnostic sample-quality assessment by identifying structurally atypical samples.

Clinical Editorial

PLOS Medicine published a clinical update in Research Highlights on 13 Jul 2026.

The item focuses on Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study.

Review the original article for the full source wording and details.