A Simple End-to-End Computer-Aided Detection Pipeline for Trained Deep Learning Models
(2024) 8th International Conference on Engineering of Computer-Based Systems, ECBS 2023 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14390 LNCS. p.259-262- Abstract
Recently, there has been a significant rise in research and development focused on deep learning (DL) models within healthcare. This trend arises from the availability of extensive medical imaging data and notable advances in graphics processing unit (GPU) computational capabilities. Trained DL models show promise in supporting clinicians with tasks like image segmentation and classification. However, advancement of these models into clinical validation remains limited due to two key factors. Firstly, DL models are trained on off-premises environments by DL experts using Unix-like operating systems (OS). These systems rely on multiple libraries and third-party components, demanding complex installations. Secondly, the absence of a... (More)
Recently, there has been a significant rise in research and development focused on deep learning (DL) models within healthcare. This trend arises from the availability of extensive medical imaging data and notable advances in graphics processing unit (GPU) computational capabilities. Trained DL models show promise in supporting clinicians with tasks like image segmentation and classification. However, advancement of these models into clinical validation remains limited due to two key factors. Firstly, DL models are trained on off-premises environments by DL experts using Unix-like operating systems (OS). These systems rely on multiple libraries and third-party components, demanding complex installations. Secondly, the absence of a user-friendly graphical interface for model outputs complicates validation by clinicians. Here, we introduce a conceptual Computer-Aided Detection (CAD) pipeline designed to address these two issues and enable non-AI experts, such as clinicians, to use trained DL models offline in Windows OS. The pipeline divides tasks between DL experts and clinicians, where experts handle model development, training, inference mechanisms, Grayscale Softcopy Presentation State (GSPS) objects creation, and containerization for deployment. The clinicians execute a simple script to install necessary software and dependencies. Hence, they can use a universal image viewer to analyze results generated by the models. This paper illustrates the pipeline's effectiveness through a case study on pulmonary embolism detection, showcasing successful deployment on a local workstation by an in-house radiologist. By simplifying model deployment and making it accessible to non-AI experts, this CAD pipeline bridges the gap between technical development and practical application, promising broader healthcare applications.
(Less)
- author
- Kahraman, Ali Teymur ; Fröding, Tomas ; Toumpanakis, Dimitrios ; Fridenfalk, Mikael ; Gustafsson, Christian Jamtheim LU and Sjöblom, Tobias
- organization
- publishing date
- 2024
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Computer-aided detection, deep learning, grayscale softcopy presentation state, machine learning, pulmonary embolism
- host publication
- Engineering of Computer-Based Systems : 8th International Conference, ECBS 2023, Proceedings - 8th International Conference, ECBS 2023, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- Kofroň, Jan ; Margaria, Tiziana and Seceleanu, Cristina
- volume
- 14390 LNCS
- pages
- 4 pages
- publisher
- Springer Science and Business Media B.V.
- conference name
- 8th International Conference on Engineering of Computer-Based Systems, ECBS 2023
- conference location
- Västerås, Sweden
- conference dates
- 2023-10-16 - 2023-10-18
- external identifiers
-
- scopus:85180154041
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 9783031492518
- DOI
- 10.1007/978-3-031-49252-5_23
- language
- English
- LU publication?
- yes
- id
- 78985870-a352-4cd9-ba9c-0be6f6b431ba
- date added to LUP
- 2024-01-31 14:26:40
- date last changed
- 2024-04-17 02:42:23
@inproceedings{78985870-a352-4cd9-ba9c-0be6f6b431ba,
abstract = {{<p>Recently, there has been a significant rise in research and development focused on deep learning (DL) models within healthcare. This trend arises from the availability of extensive medical imaging data and notable advances in graphics processing unit (GPU) computational capabilities. Trained DL models show promise in supporting clinicians with tasks like image segmentation and classification. However, advancement of these models into clinical validation remains limited due to two key factors. Firstly, DL models are trained on off-premises environments by DL experts using Unix-like operating systems (OS). These systems rely on multiple libraries and third-party components, demanding complex installations. Secondly, the absence of a user-friendly graphical interface for model outputs complicates validation by clinicians. Here, we introduce a conceptual Computer-Aided Detection (CAD) pipeline designed to address these two issues and enable non-AI experts, such as clinicians, to use trained DL models offline in Windows OS. The pipeline divides tasks between DL experts and clinicians, where experts handle model development, training, inference mechanisms, Grayscale Softcopy Presentation State (GSPS) objects creation, and containerization for deployment. The clinicians execute a simple script to install necessary software and dependencies. Hence, they can use a universal image viewer to analyze results generated by the models. This paper illustrates the pipeline's effectiveness through a case study on pulmonary embolism detection, showcasing successful deployment on a local workstation by an in-house radiologist. By simplifying model deployment and making it accessible to non-AI experts, this CAD pipeline bridges the gap between technical development and practical application, promising broader healthcare applications.</p>}},
author = {{Kahraman, Ali Teymur and Fröding, Tomas and Toumpanakis, Dimitrios and Fridenfalk, Mikael and Gustafsson, Christian Jamtheim and Sjöblom, Tobias}},
booktitle = {{Engineering of Computer-Based Systems : 8th International Conference, ECBS 2023, Proceedings}},
editor = {{Kofroň, Jan and Margaria, Tiziana and Seceleanu, Cristina}},
isbn = {{9783031492518}},
issn = {{1611-3349}},
keywords = {{Computer-aided detection; deep learning; grayscale softcopy presentation state; machine learning; pulmonary embolism}},
language = {{eng}},
pages = {{259--262}},
publisher = {{Springer Science and Business Media B.V.}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{A Simple End-to-End Computer-Aided Detection Pipeline for Trained Deep Learning Models}},
url = {{http://dx.doi.org/10.1007/978-3-031-49252-5_23}},
doi = {{10.1007/978-3-031-49252-5_23}},
volume = {{14390 LNCS}},
year = {{2024}},
}