Explainable deep learning methodologies for comprehensive white blood cell analysis

Abstract

The analysis of white blood cells (WBCs) is a critical aspect of health monitoring and diagnosis, providing valuable information on a patient’s immune health. Pathologists typically follow a systematic approach to this task, involving three sequential steps: localizing WBCs, analyzing their morphological attributes, and classifying them based on these features. Despite the interdependence of these processes, existing literature often fails to address their synergy comprehensively. Most current systems focus on individual tasks, such as segmentation or classification, without integrating these steps in a way that enhances their mutual strengths. Additionally, these systems rarely provide transparent explanations for their decisions, which are crucial for practical applications where interpretability and trust in automated systems are paramount. Deep learning models, in particular, are frequently criticized for their opacity, offering minimal insights into the rationale behind their predictions. Another significant limitation of existing methods is their lack of versatility. There is a growing demand for adaptable systems that can be fine-tuned on datasets with limited ground truth annotations or even none for specific tasks, while maintaining consistent effectiveness across various tasks.