Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis
The quality of datasets plays a crucial role in the successful training and deployment of deep learning models. Especially in the medical field, where system performance may impact the health of patients, clean datasets are a safety requirement for reliable predictions. Therefore, outlier detection...
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Zusammenfassung: | The quality of datasets plays a crucial role in the successful training and
deployment of deep learning models. Especially in the medical field, where
system performance may impact the health of patients, clean datasets are a
safety requirement for reliable predictions. Therefore, outlier detection is an
essential process when building autonomous clinical decision systems. In this
work, we assess the suitability of Self-Organizing Maps for outlier detection
specifically on a medical dataset containing quantitative phase images of white
blood cells. We detect and evaluate outliers based on quantization errors and
distance maps. Our findings confirm the suitability of Self-Organizing Maps for
unsupervised Out-Of-Distribution detection on the dataset at hand.
Self-Organizing Maps perform on par with a manually specified filter based on
expert domain knowledge. Additionally, they show promise as a tool in the
exploration and cleaning of medical datasets. As a direction for future
research, we suggest a combination of Self-Organizing Maps and feature
extraction based on deep learning. |
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DOI: | 10.48550/arxiv.2208.08834 |