Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound
<p dir="ltr">Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastogram...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2023
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| _version_ | 1864513520737976320 |
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| author | Mohammed Yusuf Ansari (16904523) |
| author2 | Marwa Qaraqe (10135172) Raffaella Righetti (17585967) Erchin Serpedin (3706543) Khalid Qaraqe (16896504) |
| author2_role | author author author author |
| author_facet | Mohammed Yusuf Ansari (16904523) Marwa Qaraqe (10135172) Raffaella Righetti (17585967) Erchin Serpedin (3706543) Khalid Qaraqe (16896504) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohammed Yusuf Ansari (16904523) Marwa Qaraqe (10135172) Raffaella Righetti (17585967) Erchin Serpedin (3706543) Khalid Qaraqe (16896504) |
| dc.date.none.fl_str_mv | 2023-12-06T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fonc.2023.1282536 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Unveiling_the_future_of_breast_cancer_assessment_a_critical_review_on_generative_adversarial_networks_in_elastography_ultrasound/26776513 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence generative adversarial networks elastography ultrasound breast cancer diagnosis enhancing pocket ultrasound computer-aided diagnosis artificial intelligence in medical imaging medical image synthesis image-to-image translation |
| dc.title.none.fl_str_mv | Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Oncology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fonc.2023.1282536" target="_blank">https://dx.doi.org/10.3389/fonc.2023.1282536</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1829423fe82ee5e51fd3fed4b312c1fd |
| identifier_str_mv | 10.3389/fonc.2023.1282536 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26776513 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasoundMohammed Yusuf Ansari (16904523)Marwa Qaraqe (10135172)Raffaella Righetti (17585967)Erchin Serpedin (3706543)Khalid Qaraqe (16896504)Biomedical and clinical sciencesOncology and carcinogenesisHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligencegenerative adversarial networkselastography ultrasoundbreast cancer diagnosisenhancing pocket ultrasoundcomputer-aided diagnosisartificial intelligence in medical imagingmedical image synthesisimage-to-image translation<p dir="ltr">Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.</p><h2>Other Information</h2><p dir="ltr">Published in: Frontiers in Oncology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fonc.2023.1282536" target="_blank">https://dx.doi.org/10.3389/fonc.2023.1282536</a></p>2023-12-06T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fonc.2023.1282536https://figshare.com/articles/journal_contribution/Unveiling_the_future_of_breast_cancer_assessment_a_critical_review_on_generative_adversarial_networks_in_elastography_ultrasound/26776513CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267765132023-12-06T03:00:00Z |
| spellingShingle | Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound Mohammed Yusuf Ansari (16904523) Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence generative adversarial networks elastography ultrasound breast cancer diagnosis enhancing pocket ultrasound computer-aided diagnosis artificial intelligence in medical imaging medical image synthesis image-to-image translation |
| status_str | publishedVersion |
| title | Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound |
| title_full | Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound |
| title_fullStr | Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound |
| title_full_unstemmed | Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound |
| title_short | Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound |
| title_sort | Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis Health sciences Health services and systems Information and computing sciences Artificial intelligence generative adversarial networks elastography ultrasound breast cancer diagnosis enhancing pocket ultrasound computer-aided diagnosis artificial intelligence in medical imaging medical image synthesis image-to-image translation |