---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Ka Yung
      foaf_name: Cheng, Ka Yung
      foaf_surname: Cheng
  - foaf_Person:
      foaf_givenName: Santiago
      foaf_name: Pazmino, Santiago
      foaf_surname: Pazmino
  - foaf_Person:
      foaf_givenName: Bjoern
      foaf_name: Bergh, Bjoern
      foaf_surname: Bergh
  - foaf_Person:
      foaf_givenName: Markus
      foaf_name: Lange-Hegermann, Markus
      foaf_surname: Lange-Hegermann
      foaf_workInfoHomepage: http://www.librecat.org/personId=71761
  - foaf_Person:
      foaf_givenName: Bjorn
      foaf_name: Schreiweis, Bjorn
      foaf_surname: Schreiweis
  bibo_doi: 10.3233/SHTI231208
  bibo_volume: 310
  dct_date: 2024^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0926-9630
  - http://id.crossref.org/issn/1879-8365
  - http://id.crossref.org/issn/978-1-64368-456-7
  dct_language: eng
  dct_publisher: IOS Press, Incorporated@
  dct_subject:
  - Medical image retrieval
  - data lake
  - DICOM
  - deep learning
  - elasticsearch
  dct_title: An Image Retrieval Pipeline in a Medical Data Integration Center.@
  fabio_hasPubmedId: '38269660'
...