Interpretable WBC Analysis through Pathologist-Aligned Reasoning

White blood cell (WBC) analysis, encompassing segmentation, morphological attribute analysis, and classification, is vital for diagnosing blood-related conditions. Pathologists follow a systematic workflow: identifying WBCs, analyzing structural features, and classifying them based on morphology. Existing systems, however, overlook their synergy and often address these tasks in isolation. Also, these systems are incapable of analysing global contextual information (or a holistic understanding of cell morphology) alongside subtle structural variations (or localised features). Both these limitations restrict the performance of existing systems and hinder their interpretability. To address these limitations, we propose a system that synergises segmentation, morphological attribute prediction (MoAP), and classification. It mirrors the pathologist's diagnostic process for the synergisation. Also, it proposes a novel hybrid architecture for simultaneously analysing local and global information. Specifically, our architecture combines CNNs for fine-grained structural analysis with a novel cross-attention-based Transformer decoder that emphasises global context. Experimental results on publicly available datasets demonstrate that our system outperforms state-of-the-art systems for WBC segmentation, MoAP, and WBC classification. Also, it shows that our system can be easily fine-tuned for effective cross-dataset evaluations.