Explainable Artificial Intelligence for Deep-Learning Based Classification of Cystic Fibrosis Lung Changes in MRI...19th World Congress on Medical and Health Informatics, July 8-12, 2023, New South Wales, Australia
Algorithms increasing the transparence and explain ability of neural networks are gaining more popularity. Applying them to custom neural network architectures and complex medical problems remains challenging. In this work, several algorithms such as integrated gradients and grad came were used to g...
| Publicado en: | Studies in Health Technology & Informatics Vol. 310; pp. 921 - 926 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2023
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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=175248909&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175248909 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2023 vid: 310 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 175248909 175248909 175248909 10.3233/SHTI231099 175248909 ppf: 921 ppct: 5 formats: tig: atl: Explainable Artificial Intelligence for Deep-Learning Based Classification of Cystic Fibrosis Lung Changes in MRI...19th World Congress on Medical and Health Informatics, July 8-12, 2023, New South Wales, Australia aug: au: RINGWALD, Friedemann G. MARTYNOVA, Anna MIERISCH, Julian WIELPÜTZ, Mark EISENMANN, Urs affil: Institute of Medical Informatics, Heidelberg University Hospital, Germany sug: subj: Lung Pathology Cystic Fibrosis Diagnosis Cystic Fibrosis Classification Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Methods Artificial Intelligence Utilization Deep Learning Congresses and Conferences New South Wales New South Wales Human Neural Networks (Computer) Algorithms Evaluation Contrast Media Diagnostic Use Severity of Illness Predictive Value of Tests ab: Algorithms increasing the transparence and explain ability of neural networks are gaining more popularity. Applying them to custom neural network architectures and complex medical problems remains challenging. In this work, several algorithms such as integrated gradients and grad came were used to generate additional explainable outputs for the classification of lung perfusion changes and mucus plugging in cystic fibrosis patients on MRI. The algorithms are applied on top of an already existing deep learning-based classification pipeline. From six explain ability algorithms, four were implemented successfully and one yielded satisfactory results which might provide support to the radiologist. It was evident, that the areas relevant for the classification were highlighted, thus emphasizing the applicability of deep learning for classification of lung changes in CF patients. Using explainable concepts with deep learning could improve confidence of clinicians towards deep learning and introduction of more diagnostic decision support systems. pubtype: Academic Journal doctype: diagnostic images proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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