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An Image Retrieval Pipeline in a Medical Data Integration Center
Ka Yung Cheng, Santiago Pazmino, Björn Bergh, Markus Lange-Hegermann, Björn Schreiweis
1388 - 1389
10.3233/SHTI231208
Research Article
Studies in Health Technology and Informatics
Volume 310: MEDINFO 2023 — The Future Is Accessible
Abstract

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.

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