Paper ID: 2208.08834

Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis

Stefan Röhrl, Alice Hein, Lucie Huang, Dominik Heim, Christian Klenk, Manuel Lengl, Martin Knopp, Nawal Hafez, Oliver Hayden, Klaus Diepold

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.

Submitted: Aug 18, 2022