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

arXiv: https://arxiv.org/abs/2208.08834 · HTML: https://mlsanity.com/paper/2208.08834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 18 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Quantitative Phase Imaging and Artificial Intelligence: A Review](https://mlsanity.com/paper/1806.03982/) (2018)

## Built on by (most cited first, 1 of 1)

- [Towards Interpretable Classification of Leukocytes based on Deep Learning](https://mlsanity.com/paper/2311.14485/) (2023)

## Abstract (arXiv)

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.