Work

Investigating the Impact of Imbalanced Medical Data on the Performance of Self-Supervised Learning Approaches

Year: 2025

Type: conference-paper

Abstract: In clinical practice, a substantial amount of data is generated on a daily basis for diagnostic purposes.Since expensive expert knowledge is required for data annotation in order to use this data for supervised learning, large amounts of data often remain unused.Self-supervised learning methods are well suited for using unlabeled data by pre-training networks to solve pretext tasks.As medical data follow an underlying uneven distribution of occurring diseases, they are inherently imbalanced.This could introduce an unwanted bias during pre-training, ultimately leading to negative consequences that may inhibit the benefits of finetuning.In this work we investigate the impact of the imbalance of 2D and 3D medical datasets used for pre-training, as well as the importance of the type and size of the dataset used for pre-training and the pretext task.Our findings indicate that the size of the dataset used for pre-training has greater impact on the final tasks than its balance.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-127

Authors: Manuel Laufer, Felicitas Brokmann, Dominik Mairhöfer, Erhardt Barth, Thomas Martinetz

Institution: University of Lübeck

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

OpenAlex ID: W4409480501

FWCI:

Cites: 10

Cited by:

Related to: 10