论文标题
及时：改善细胞类型分类医学成像中的标签一致性
TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification
论文作者
论文摘要
诊断白血病或贫血等疾病需要可靠的血细胞计数。血液学医生通常手动标记并计算血细胞的显微镜图像。然而，在许多情况下，不同成熟度状态的细胞很难区分，并且与图像噪声和主观性结合使用，人类容易犯错误。这导致通常不可再现的标签，这会直接影响诊断。我们及时介绍了一个概率模型，将假次推理方法与不均匀的隐藏马尔可夫树相结合，该方法解决了标签不一致的这一挑战。我们首先在仿真数据上显示，及时能够识别和纠正与标签校正的基线方法更高精度和回忆的错误标签。然后，我们将方法应用于两个现实世界数据集的血细胞数据数据集，并表明及时成功地发现了不一致的标签，从而提高了人类生成的标签的质量。
Diagnosing diseases such as leukemia or anemia requires reliable counts of blood cells. Hematologists usually label and count microscopy images of blood cells manually. In many cases, however, cells in different maturity states are difficult to distinguish, and in combination with image noise and subjectivity, humans are prone to make labeling mistakes. This results in labels that are often not reproducible, which can directly affect the diagnoses. We introduce TIMELY, a probabilistic model that combines pseudotime inference methods with inhomogeneous hidden Markov trees, which addresses this challenge of label inconsistency. We show first on simulation data that TIMELY is able to identify and correct wrong labels with higher precision and recall than baseline methods for labeling correction. We then apply our method to two real-world datasets of blood cell data and show that TIMELY successfully finds inconsistent labels, thereby improving the quality of human-generated labels.
