# HemAutomaton

Automated blood and bone marrow smear analysis had been confined to manually selected regions of interest because gigapixel whole-slide scanning is computationally heavy. HemAutomaton uses a neural cellular automaton to locate white blood cells across entire slides without manual region selection, matching Faster R-CNN in-domain detection and reaching a mean cross-domain F1 of 0.79 versus 0.65, with 3.3k trainable parameters against 41.4M. Across three blood cancer cohorts it extracted 108 million single-cell images and performed automated differential counting through few-shot classification with a pretrained hematology foundation model.

Automated blood and bone marrow smear analysis had been confined to manually selected regions of interest because gigapixel whole-slide scanning is computationally heavy. HemAutomaton uses a neural cellular automaton to locate white blood cells across entire slides without manual region selection, matching Faster R-CNN in-domain detection and reaching a mean cross-domain F1 of 0.79 versus 0.65, with 3.3k trainable parameters against 41.4M. Across three blood cancer cohorts it extracted 108 million single-cell images and performed automated differential counting through few-shot classification with a pretrained hematology foundation model.