TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification
This work addresses labeling reproducibility issues in medical diagnostics for diseases like leukemia or anemia, though it is incremental as it builds on existing pseudotime and hidden Markov methods.
The paper tackles the problem of inconsistent labeling in medical imaging for blood cell classification by introducing TIMELY, a probabilistic model that combines pseudotime inference with hidden Markov trees, which improved precision and recall in correcting wrong labels on simulation data and enhanced label quality on real-world datasets.
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.