LAUSR

Am J Clin Pathol 2018;150:S143-S168 DOI: 10.1093/AJCP/AQY112 unclassifiable cells and naked/smudged nuclei were annotated as unknown (3,160). Areas with red blood cells and debris within ROIs were carefully circled for exclusion. Challenges we discovered include exhaustively annotating every cell in the ROIs, to avoid bias, which yields higher numbers of cells in the unknown category, and excluding often abundant red blood cells and debris that were intermingled with the nucleated cells of interest. The other main challenges for automating cell counts are detecting and localizing cells with complex and varying morphologies, distinguishing closely packed cells, and employing localizations for cell classification. The large number of classes, subtle interclass differences, and variations in smear preparation and staining (color and contrast) represented additional, distinct obstacles we identified for BMA analysis. Lastly, the collected data set had an imbalanced distribution among classes, which was unavoidable due to the natural uneven distribution of cell types. This creates a problem in training the algorithm, as only a few features are learned from classes that have limited data. We addressed this problem by annotating additional cells from these classes, without the constraint of ROIs, and will use oversampling and augmentation techniques when training the algorithms. Having created a promising training data set, we are testing this exciting new algorithm on another data set with the ultimate goal of devising a reliable, objective method that automates DCCs.