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.

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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
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article

Comparing Human‐Level and Machine Learning Model Performance in White Blood Cell Morphology Assessment

Christina Brown, P.D. Lawrence
1 citations
European Journal Of Haematology
Digital Imaging for Blood Diseases
0.25
article

Comparing Human‐Level and Machine Learning Model Performance in White Blood Cell Morphology Assessment

Christina Brown, P.D. Lawrence
article en
1 citations

Abstract

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.

European Journal Of HaematologyVol. 114(1)
Royal Prince Alfred Hospital (AU), Princess Alexandra Hospital (AU)
Quality Education
Openalex Percentile: Top 46%
Digital Imaging for Blood Diseases
0.25
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