Abstract
The characterization and classification of white blood cells (WBC) is critical for the diagnosis of anemia, leukemia and many other hematologic diseases. We developed WBC-Profiler, an unsupervised feature learning system for quantitative analysis of leukocytes. We demonstrate that WBC-Profiler enables automatic extraction of complex signatures from microscopic images without human-intervention and thereafter effective construction of leukocytes profiles, which decouples large scale complex leukocytes characterization from limitations in both human-based feature engineering/optimization and the end-to-end solutions provided by modern deep neural networks, and therefore has the potential to provide new opportunities towards meaningful studies/applications with scientific and/or clinical impact