Blood and bone marrow smears are central to diagnosing hematological disorders. While whole-slide scanning enables automated single-cell profiling at scale, the computational burden of gigapixel images has confined automated analysis to manually pre-selected regions of interest. Here, we present HemAutomaton, a framework built on neural cellular automata that extracts white blood cells across entire whole-slide images without manual region selection and with minimal annotation requirements. HemAutomaton matches the in-domain detection accuracy of Faster R-CNN while surpassing it under domain shifts, operating at a fraction of the computational cost and with over 10,000-fold fewer parameters. Across three blood cancer cohorts, we extract over 100 million single-cell images and achieve annotation-efficient differential counting via few-shot classification using a pretrained hematology foundation model. Slide-wide spatial analysis uncovers consistent blast-frequency gradients across smears and demonstrates that automated blast counts match expert evaluations within inter-expert variability. Scaling automated profiling from hundreds to tens of thousands of cells per smear overcomes conventional sampling limits and establishes the foundation for more sensitive AI supported hematological assessments.