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A comparative study between paired and unpaired image quality assessment in low-dose CT denoising
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik.
Campus Bio-Medico University of Rome, Research Unit of Computer Systems and Bioinformatics, Rome, Italy.
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Radiofysik. Research Unit of Computer Systems and Bioinformatics Campus Bio-Medico University of Rome Rome, Italy.ORCID-id: 0000-0003-2621-072X
2023 (Engelska)Ingår i: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS): Proceedings, IEEE, 2023, s. 471-476Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The current deep learning approaches for low-dose CT denoising can be divided into paired and unpaired methods. The former involves the use of well-paired datasets, whilst the latter relaxes this constraint. The large availability of unpaired datasets has raised the interest in deepening unpaired denoising strategies that, in turn, need for robust evaluation techniques going beyond the qualitative evaluation. To this end, we can use quantitative image quality assessment scores that we divided into two categories, i.e., paired and unpaired measures. However, the interpretation of unpaired metrics is not straightforward, also because the consistency with paired metrics has not been fully investigated. To cope with this limitation, in this work we consider 15 paired and unpaired scores, which we applied to assess the performance of low-dose CT denoising. We perform an in-depth statistical analysis that not only studies the correlation between paired and unpaired metrics but also within each category. This brings out useful guidelines that can help researchers and practitioners select the right measure for their applications.

Ort, förlag, år, upplaga, sidor
IEEE, 2023. s. 471-476
Serie
Proceedings - IEEE International Symposium on Computer-Based Medical Systems, ISSN 1063-7125
Nyckelord [en]
generative adversarial network, IQA, metrics, Unpaired evaluation
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:umu:diva-212825DOI: 10.1109/CBMS58004.2023.00264Scopus ID: 2-s2.0-85166479763ISBN: 9798350312249 (digital)OAI: oai:DiVA.org:umu-212825DiVA, id: diva2:1788472
Konferens
36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023, L'Aquila, Italy, June 22-24, 2023
Tillgänglig från: 2023-08-16 Skapad: 2023-08-16 Senast uppdaterad: 2023-08-16Bibliografiskt granskad

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Di Feola, FrancescoSoda, Paolo

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