DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, SeokHwan | ko |
dc.contributor.author | Kim, Myeong-Gee | ko |
dc.contributor.author | Kim, Youngmin | ko |
dc.contributor.author | Jung, Guil | ko |
dc.contributor.author | Kwon, Hyuksool | ko |
dc.contributor.author | Bae, Hyeon-Min | ko |
dc.date.accessioned | 2022-12-22T03:03:15Z | - |
dc.date.available | 2022-12-22T03:03:15Z | - |
dc.date.created | 2022-12-21 | - |
dc.date.issued | 2022-09 | - |
dc.identifier.citation | 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, pp.780 - 789 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10203/303496 | - |
dc.description.abstract | Recent improvements in deep learning have brought great progress in ultrasonic lesion quantification. However, the learning-based scheme performs properly only when a certain level of similarity between train and test condition is ensured. However, real-world test condition expects diverse untrained probe geometry from various manufacturers, which undermines the credibility of learning-based ultrasonic approaches. In this paper, we present a meta-learned deformable sensor generalization network that generates consistent attenuation coefficient (AC) image regardless of the probe condition. The proposed method was assessed through numerical simulation and in-vivo breast patient measurements. The numerical simulation shows that the proposed network outperforms existing state-of-the-art domain generalization methods for the AC reconstruction under unseen probe conditions. In in-vivo studies, the proposed network provides consistent AC images irrespective of various probe conditions and demonstrates great clinical potential in differential breast cancer diagnosis. | - |
dc.language | English | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | Sensor Geometry Generalization to Untrained Conditions in Quantitative Ultrasound Imaging | - |
dc.type | Conference | - |
dc.identifier.wosid | 000867434800074 | - |
dc.identifier.scopusid | 2-s2.0-85139106951 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 780 | - |
dc.citation.endingpage | 789 | - |
dc.citation.publicationname | 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 | - |
dc.identifier.conferencecountry | SI | - |
dc.identifier.conferencelocation | Singapore | - |
dc.identifier.doi | 10.1007/978-3-031-16446-0_74 | - |
dc.contributor.localauthor | Bae, Hyeon-Min | - |
dc.contributor.nonIdAuthor | Kwon, Hyuksool | - |
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