Comparing Human‐Level and Machine Learning Model Performance in White Blood Cell Morphology Assessment
The substantial, though not perfect, agreement between human observers highlights the inherent subjectivity in white blood cell morphologic assessment. A machine learning model performed similarly to human observers in single white blood cell identification. Further research is needed to compare human-level and machine learning performance in ways that more closely reflect the typical process of morphologic assessment.
Authors
- Christina Brown (ORCID: https://orcid.org/0000-0003-2811-8943)
- P.D. Lawrence
Institutions
- Royal Prince Alfred Hospital (AU)
- Princess Alexandra Hospital (AU)
Publication Details
- Journal
- European Journal Of Haematology
- Published
- 2024-10-06
- DOI
- https://doi.org/10.1111/ejh.14318
- Citations
- 1
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.25