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An Image Retrieval Pipeline in a Medical Data Integration Center

Ka Yung Cheng , Santiago Pazmino , Björn Bergh , Markus Lange-Hegermann und Björn Schreiweis,
Jan 2024

Medical images need annotations with high-level semantic descriptors, so that domain experts can search for the desired dataset among an enormous volume of visual media within a Medical Data Integration Center. This article introduces a processing pipeline for storing and annotating DICOM and PNG imaging data by applying Elasticsearch, S3 and Deep Learning technologies. The proposed method processes both DICOM and PNG images to generate annotations. These image annotations are indexed in Elasticsearch with the corresponding raw data paths, where they can be retrieved and analyzed.

@misc{2959,
author= {Cheng, Ka Yung and Pazmino, Santiago and Bergh, Björn and Lange-Hegermann, Markus and Schreiweis, Björn},
title= {An Image Retrieval Pipeline in a Medical Data Integration Center},
howpublished= {Poster: World Congress on Medical and Health Informatics},
month= {Jan},
year= {2024},
note= {},
}