The characterization and classification of white blood cells (WBC) are 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, through independent validation, that WBC-Profiler enables automatic extraction of complex and robust signatures from microscopic images without human-intervention and, thereafter, effective construction of interpretable leukocyte profiles, which decouples large scale complex leukocyte characterization from limitations in both human-based feature engineering/optimization and the end-to-end solutions provided by many modern deep neural networks. Further evaluation in a real-world clinical setting confirms that, compared with 23 clinicians from 8 hospitals (class-average-sensitivity, 0.798; class-average-specificity, 0.963; cell-average-timecost: 3.158 s), WBC-Profiler performs with significantly improved accuracy and speed (class-average-sensitivity, 0.890; class-average-specificity, 0.980; cell-average-timecost: 0.375 s). Our findings suggest that WBC-Profiler has the potential clinical implications.

白细胞（WBC）的表征和分类对于贫血，白血病和许多其他血液学疾病的诊断至关重要。我们开发了WBC-Profiler，这是一种用于定量分析白细胞的无监督特征学习系统。我们通过独立验证证明，WBC-Profiler无需人工干预即可自动从显微图像中提取复杂而强大的特征，然后有效构建可解释的白细胞谱，从而将大规模复杂白细胞的表征与人类-人的局限性脱钩许多现代深度神经网络提供的基于特性的工程/优化和端到端解决方案。在实际临床环境中的进一步评估证实，与来自8家医院的23名临床医生（平均敏感度为0.798;平均特异性为0.963;细胞平均时间成本：3.158 s）相比，WBC-Profiler的准确度和速度显着提高（平均敏感度，0.890；类平均特异性，0.980；细胞平均时间成本：0.375 s）。我们的发现表明，WBC-Profiler具有潜在的临床意义。