Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project.
Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It come...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2612 - 2627 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181515413&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515413 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515413 181515413 181515413 10.1007/s10278-024-01110-0 181515413 ppf: 2612 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project. aug: au: Diot-Dejonghe, Tiphaine Leporq, Benjamin Bouhamama, Amine Ratiney, Helene Pilleul, Frank Beuf, Olivier Cervenansky, Frederic affil: INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69XXX, Lyon, France sug: subj: Osteosarcoma Therapy Treatment Outcomes Evaluation Diagnostic Imaging Methods Image Interpretation, Computer Assisted Software Design Deep Learning Radiomics Human Medical Informatics Machine Learning Monitoring, Physiologic Internet Data Security Access to Information Individualized Medicine Funding Source ab: Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It comes with the strong need to exploit and visualize large volumes of images and associated medical data. The work carried out in this paper follows on from a main case study piloted in a cancer center. It proposes an analysis pipeline for patients with osteosarcoma through segmentation, feature extraction and application of a deep learning model to predict response to treatment. The main aim of the AWESOMME project is to leverage this work and implement the pipeline on an easy-to-access, secure web platform. The proposed WEB application is based on a three-component architecture: a data server, a heavy computation and authentication server and a medical imaging web-framework with a user interface. These existing components have been enhanced to meet the needs of security and traceability for the continuous production of expert data. It innovates by covering all steps of medical imaging processing (visualization and segmentation, feature extraction and aided diagnostic) and enables the test and use of machine learning models. The infrastructure is operational, deployed in internal production and is currently being installed in the hospital environment. The extension of the case study and user feedback enabled us to fine-tune functionalities and proved that AWESOMME is a modular solution capable to analyze medical data and share research algorithms with in-house clinicians. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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