Despite the high number of machine learning models presented in the last few years for automatically annotating medical images with deep learning models, clear baselines to compare methods upon are still missing. We present an initial set of experimentations of a standard encoder-decoder architecture with the Indiana University Chest X-ray dataset. The experiments include different convolutional architectures and decoding strategies for the recurrent decoder module. The results here presented could potentially benefit those tackling the same task in languages with fewer linguistic resources than those available in English.
Baselines for Automatic Medical Image Reporting
Franco Alberto Cardillo
Primo
2023
Abstract
Despite the high number of machine learning models presented in the last few years for automatically annotating medical images with deep learning models, clear baselines to compare methods upon are still missing. We present an initial set of experimentations of a standard encoder-decoder architecture with the Indiana University Chest X-ray dataset. The experiments include different convolutional architectures and decoding strategies for the recurrent decoder module. The results here presented could potentially benefit those tackling the same task in languages with fewer linguistic resources than those available in English.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.anceserie | LECTURE NOTES IN NETWORKS AND SYSTEMS | en |
| dc.authority.orgunit | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | en |
| dc.authority.people | Franco Alberto Cardillo | en |
| dc.authority.project | corda__h2020::a0520286b7e6ad6e9c871d7ab8f2c196 | en |
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| dc.date.firstsubmission | 2024/10/08 11:21:53 | * |
| dc.date.issued | 2023 | - |
| dc.date.submission | 2024/10/08 11:21:53 | * |
| dc.description.abstracteng | Despite the high number of machine learning models presented in the last few years for automatically annotating medical images with deep learning models, clear baselines to compare methods upon are still missing. We present an initial set of experimentations of a standard encoder-decoder architecture with the Indiana University Chest X-ray dataset. The experiments include different convolutional architectures and decoding strategies for the recurrent decoder module. The results here presented could potentially benefit those tackling the same task in languages with fewer linguistic resources than those available in English. | - |
| dc.description.allpeople | Cardillo, FRANCO ALBERTO | - |
| dc.description.allpeopleoriginal | Franco Alberto Cardillo | en |
| dc.description.fulltext | restricted | en |
| dc.description.numberofauthors | 1 | - |
| dc.identifier.doi | 10.1007/978-3-031-29717-5_4 | en |
| dc.identifier.isbn | 978-3-031-29717-5 | en |
| dc.identifier.scopus | 2-s2.0-85152533695 | - |
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| dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-031-29717-5_4 | en |
| dc.language.iso | eng | en |
| dc.publisher.country | CHE | en |
| dc.publisher.name | Springer International Publishing | en |
| dc.publisher.place | Springer Nature Switzerland AG | en |
| dc.relation.conferencedate | 19-20 maggio, 2022 | en |
| dc.relation.conferencename | 1st Serbian International Conference on Applied Artificial Intelligence | en |
| dc.relation.conferenceplace | Kragujevac, Serbia | en |
| dc.relation.firstpage | 58 | en |
| dc.relation.ispartofbook | Applied Artificial Intelligence: Medicine, Biology, Chemistry, Financial, Games, Engineering | en |
| dc.relation.lastpage | 74 | en |
| dc.relation.numberofpages | 17 | en |
| dc.relation.projectAcronym | DeepHealth | en |
| dc.relation.projectAwardNumber | 825111 | en |
| dc.relation.projectAwardTitle | Deep-Learning and HPC to Boost Biomedical Applications for Health | en |
| dc.relation.projectFunderName | European Commission | en |
| dc.relation.projectFundingStream | Horizon 2020 Framework Programme | en |
| dc.relation.volume | 659 | en |
| dc.subject.keywordseng | Medical Image Analysis | - |
| dc.subject.keywordseng | Neural Language Generation | - |
| dc.subject.keywordseng | Deep Learning | - |
| dc.subject.singlekeyword | Medical Image Analysis | * |
| dc.subject.singlekeyword | Neural Language Generation | * |
| dc.subject.singlekeyword | Deep Learning | * |
| dc.title | Baselines for Automatic Medical Image Reporting | en |
| dc.type.circulation | Internazionale | en |
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| dc.type.referee | Comitato scientifico | en |
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| iris.scopus.extIssued | 2023 | - |
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| scopus.contributor.affiliation | Institute for Computational Linguistics | - |
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| scopus.contributor.country | Italy | - |
| scopus.contributor.dptid | 104078586 | - |
| scopus.contributor.name | Franco Alberto | - |
| scopus.contributor.subaffiliation | National Research Council; | - |
| scopus.contributor.surname | Cardillo | - |
| scopus.date.issued | 2023 | * |
| scopus.description.abstracteng | Despite the high number of machine learning models presented in the last few years for automatically annotating medical images with deep learning models, clear baselines to compare methods upon are still missing. We present an initial set of experimentations of a standard encoder-decoder architecture with the Indiana University Chest X-ray dataset. The experiments include different convolutional architectures and decoding strategies for the recurrent decoder module. The results here presented could potentially benefit those tackling the same task in languages with fewer linguistic resources than those available in English. | * |
| scopus.description.allpeopleoriginal | Cardillo F.A. | * |
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| scopus.relation.conferencedate | 2022 | * |
| scopus.relation.conferencename | 1st Serbian International Conference on Applied Artificial Intelligence, SICAAI 2022 | * |
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| scopus.relation.firstpage | 58 | * |
| scopus.relation.lastpage | 74 | * |
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| scopus.subject.keywords | Computer Vision; Image Classification; Medical Imaging; Neural Language Generation; | * |
| scopus.title | Baselines for Automatic Medical Image Reporting | * |
| scopus.titleeng | Baselines for Automatic Medical Image Reporting | * |
| Appare nelle tipologie: | 04.01 Contributo in Atti di convegno | |
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