mlsanityData to 7 Oct 2026

Towards Interpretable Classification of Leukocytes based on Deep Learning

arXiv 2311.14485 ↗First posted 24 Nov 2023cs.CV cs.LGcited by 0

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Abstract (arXiv)

Label-free approaches are attractive in cytological imaging due to their flexibility and cost efficiency. They are supported by machine learning methods, which, despite the lack of labeling and the associated lower contrast, can classify cells with high accuracy where the human observer has little chance to discriminate cells. In order to better integrate these workflows into the clinical decision making process, this work investigates the calibration of confidence estimation for the automated classification of leukocytes. In addition, different visual explanation approaches are compared, which should bring machine decision making closer to professional healthcare applications. Furthermore, we were able to identify general detection patterns in neural networks and demonstrate the utility of the presented approaches in different scenarios of blood cell analysis.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 67 references that are in the map. First posted on arXiv 24 Nov 2023.

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22 of 67 references.

References · 22

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10 more
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  10. 2022Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysiscited by 1

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