How Data Infrastructure Deals with Bias Problems in Medical Imaging.

Li, Feifei; Kutafina, Ekaterina; Schoneck, Mirjam; Caldeira, Liliana Lourenco; Beyan, Oya

Li, Feifei; Kutafina, Ekaterina; Schoneck, Mirjam; Caldeira, Liliana Lourenco; Beyan, Oya.

Afiliación

Li F; Institute for Biomedical Informatics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Germany.
Kutafina E; Institute for Biomedical Informatics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Germany.
Schoneck M; Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Germany.
Caldeira LL; Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Germany.
Beyan O; Institute for Biomedical Informatics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Germany.

Stud Health Technol Inform ; 316: 726-730, 2024 Aug 22.

Article en En | MEDLINE | ID: mdl-39176898

ABSTRACT

ABSTRACT

The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithms and generative models. It introduces a taxonomy of bias problems and addresses them through a data infrastructure initiative the PADME (Platform for Analytics and Distributed Machine-Learning for Enterprises), which is a part of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) project. The PADME facilitates the structuring and sharing of health data while ensuring privacy and adherence to FAIR principles. The paper presents experimental results that show that generative methods can be effective in data augmentation. Complying with PADME infrastructure, this work proposes a solution framework to deal with bias in the different data stations and preserve privacy when transferring images. It highlights the importance of standardized data infrastructure in mitigating biases and promoting FAIR, reusable, and privacy-preserving research environments in healthcare.

Asunto(s)

Diagnóstico por Imagen; Aprendizaje Automático; Humanos; Sesgo; Algoritmos; Confidencialidad; Seguridad Computacional

Palabras clave

Bias; Data Infrastructure; Differential Privacy; Federated Learning; Machine Learning; Medical Imaging

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diagnóstico por Imagen / Aprendizaje Automático Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Países Bajos

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