TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification

TL;DR

TIMELY is a probabilistic model that enhances labeling consistency in medical blood cell images by identifying and correcting inconsistent labels, thereby improving diagnostic reliability.

Contribution

The paper introduces TIMELY, a novel probabilistic model combining pseudotime inference and hidden Markov trees for correcting labeling errors in medical imaging.

Findings

TIMELY outperforms baseline methods in simulation data for label correction.

It successfully identifies inconsistent labels in real-world blood cell datasets.

Improves overall quality and reproducibility of human-generated labels.

Abstract

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.