---
_id: '12822'
abstract:
- lang: eng
text: A medical data integration center integrates a large volume of medical images
from clinical departments, including X-rays, CT scans, and MRI scans. Ideally,
all images should be indexed appropriately with standard clinical terms. However,
some images have incorrect or missing annotations, which creates challenges in
searching and integrating data centrally. To address this issue, accurate and
meaningful descriptors are needed for indexing fields, enabling users to efficiently
search for desired images and integrate them with international standards. This
paper aims to provide concise annotation for missing or incorrectly indexed fields,
incorporating essential instance -level information such as radiology modalities
(e.g., X-rays), anatomical regions (e.g., chest), and body orientations (e.g.,
lateral) using a Deep Learning classification model - ResNet50. To demonstrate
the capabilities of our algorithm in generating annotations for indexing fields,
we conducted three experiments using two opensource datasets, the ROCO dataset,
and the IRMA dataset, along with a custom dataset featuring SNOMED CT labels.
While the outcomes of these experiments are satisfactory (Precision of >75%) for
less critical tasks and serve as a valuable testing ground for image retrieval,
they also underscore the need for further exploration of potential challenges.
This essay elaborates on the identified issues and presents well-founded recommendations
for refining and advancing our proposed approach.
author:
- first_name: Ka Yung
full_name: Cheng, Ka Yung
last_name: Cheng
- first_name: Markus
full_name: Lange-Hegermann, Markus
id: '71761'
last_name: Lange-Hegermann
- first_name: Jan-Bernd
full_name: Hövener, Jan-Bernd
last_name: Hövener
- first_name: Björn
full_name: Schreiweis, Björn
last_name: Schreiweis
citation:
ama: Cheng KY, Lange-Hegermann M, Hövener JB, Schreiweis B. Instance-level medical
image classification for text-based retrieval in a medical data integration center.
Computational and Structural Biotechnology Journal. 2024;24:434-450. doi:10.1016/j.csbj.2024.06.006
apa: Cheng, K. Y., Lange-Hegermann, M., Hövener, J.-B., & Schreiweis, B. (2024).
Instance-level medical image classification for text-based retrieval in a medical
data integration center. Computational and Structural Biotechnology Journal,
24, 434–450. https://doi.org/10.1016/j.csbj.2024.06.006
bjps: Cheng KY et al. (2024) Instance-Level Medical Image Classification
for Text-Based Retrieval in a Medical Data Integration Center. Computational
and Structural Biotechnology Journal 24, 434–450.
chicago: 'Cheng, Ka Yung, Markus Lange-Hegermann, Jan-Bernd Hövener, and Björn Schreiweis.
“Instance-Level Medical Image Classification for Text-Based Retrieval in a Medical
Data Integration Center.” Computational and Structural Biotechnology Journal
24 (2024): 434–50. https://doi.org/10.1016/j.csbj.2024.06.006.'
chicago-de: 'Cheng, Ka Yung, Markus Lange-Hegermann, Jan-Bernd Hövener und Björn
Schreiweis. 2024. Instance-level medical image classification for text-based retrieval
in a medical data integration center. Computational and Structural Biotechnology
Journal 24: 434–450. doi:10.1016/j.csbj.2024.06.006,
.'
din1505-2-1: 'Cheng, Ka Yung ; Lange-Hegermann, Markus ; Hövener,
Jan-Bernd ; Schreiweis, Björn:
Instance-level medical image classification for text-based retrieval in a medical
data integration center. In: Computational and Structural Biotechnology Journal
Bd. 24. Amsterdam [u.a.], Elsevier BV (2024), S. 434–450'
havard: K.Y. Cheng, M. Lange-Hegermann, J.-B. Hövener, B. Schreiweis, Instance-level
medical image classification for text-based retrieval in a medical data integration
center, Computational and Structural Biotechnology Journal. 24 (2024) 434–450.
ieee: 'K. Y. Cheng, M. Lange-Hegermann, J.-B. Hövener, and B. Schreiweis, “Instance-level
medical image classification for text-based retrieval in a medical data integration
center,” Computational and Structural Biotechnology Journal, vol. 24, pp.
434–450, 2024, doi: 10.1016/j.csbj.2024.06.006.'
mla: Cheng, Ka Yung, et al. “Instance-Level Medical Image Classification for Text-Based
Retrieval in a Medical Data Integration Center.” Computational and Structural
Biotechnology Journal, vol. 24, 2024, pp. 434–50, https://doi.org/10.1016/j.csbj.2024.06.006.
short: K.Y. Cheng, M. Lange-Hegermann, J.-B. Hövener, B. Schreiweis, Computational
and Structural Biotechnology Journal 24 (2024) 434–450.
ufg: 'Cheng, Ka Yung u. a.: Instance-level medical image classification for
text-based retrieval in a medical data integration center, in: Computational
and Structural Biotechnology Journal 24 (2024), S. 434–450.'
van: Cheng KY, Lange-Hegermann M, Hövener JB, Schreiweis B. Instance-level medical
image classification for text-based retrieval in a medical data integration center.
Computational and Structural Biotechnology Journal. 2024;24:434–50.
date_created: 2025-04-22T13:32:38Z
date_updated: 2025-06-26T08:58:59Z
department:
- _id: DEP5023
doi: 10.1016/j.csbj.2024.06.006
external_id:
isi:
- '001257361300001'
pmid:
- '38975287'
intvolume: ' 24'
isi: '1'
keyword:
- DICOM images
- Medical image captioning
- Medical image interchange
- SNOMED CT body structure
language:
- iso: eng
page: 434-450
place: Amsterdam [u.a.]
pmid: '1'
publication: Computational and Structural Biotechnology Journal
publication_identifier:
issn:
- 2001-0370
publication_status: published
publisher: Elsevier BV
status: public
title: Instance-level medical image classification for text-based retrieval in a medical
data integration center
type: scientific_journal_article
user_id: '83781'
volume: 24
year: '2024'
...