LeukemiaCellNet: A Patient-Aware Multi-Scale CNN–Transformer Framework with Attention-Based Aggregation for Leakage-Free AML Cytomorphology Classification

In this work, LeukemiaCellNet is a patient-aware multi-scale CNN–Transformer framework for leakage-free AML cytomorphology classification. The proposed method fuses a multi-scale CNN with a global Transformer to extract the local morphological features and global contextual features, respectively, to represent the complementary attributes of cells. To integrate cell-level features, an attention aggregation mechanism at the patient level is introduced, and consistency regularization is used to enhance the stability of prediction in different patient samples. Patient-disjoint evaluation addresses the limitation of information leakage and allows obtaining a more accurate evaluation of the use of AI for AML classification in clinical decision support.