Informatica

Accurately differentiating between fungal and bacterial infections is critical for treatment but remains challenging due to slow manual diagnostics and class imbalance in medical datasets. This paper introduces OD-Abs, a one-class outlier detection classifier based on an autoencoder architecture to distinguish infection types in microscopic images. By training solely on the reference fungal class, the model learns to identify bacterial instances as outliers. Our approach yields statistically significant improvements compared to other one-class approaches, achieving an F1-score of 0.967, an AUC of 0.980, and a specificity of 0.992. Additionally, we explore the explainability of our method by analysing reconstruction error maps from both a technical and biological perspective and conclude that the model’s decision-making aligns with biologically relevant morphological features. This proof of concept study establishes the OD-Abs autoencoder model as a highly robust tool for handling the task of classifying images of bacteria and fungi from abscesses.

A novel approach to outlier detection on the ground of the properties of distribution of distances between multidimensional points is presented. The basic idea is to evaluate the outlier factor for each data point. The factor is used to rank the dataset objects regarding their degree of being an outlier. Selecting the points with the minimal factor values can then identify outliers. The main advantages of the approach are: (1) no parameter choice in outlier detection is necessary; (2) detection is not dependent on clustering algorithms.