Abstract--Breast cancer is the most frequently diagnosed non-skin cancer in women, the second leading cause of death among women. Early detection of a breast cancer is fundamental for ensuring high survival rate. Imaging techniques are used to identify suspicious modifications of breast tissue. Among these, contrast-enhanced magnetic resonance imaging (CE-MRI) is particularly interesting for its lack of exposure to radiation and its ability to highlight differences in vascularisation, typical of cancer lesions. Automatic or semi-automatic methods are especially useful with this technique, due to the high quantity of data, in the form of 4D images (3D space + time), to be analysed in each test. This survey describes approaches to fully automatic computer-aided detection/diagnosis of breast lesions with CE-MRI, with particular emphasis on computational intelligence techniques.
Automatic Approaches for CE-MRI Examination of the Breast: A Survey
FA Cardillo;
2017
Abstract
Abstract--Breast cancer is the most frequently diagnosed non-skin cancer in women, the second leading cause of death among women. Early detection of a breast cancer is fundamental for ensuring high survival rate. Imaging techniques are used to identify suspicious modifications of breast tissue. Among these, contrast-enhanced magnetic resonance imaging (CE-MRI) is particularly interesting for its lack of exposure to radiation and its ability to highlight differences in vascularisation, typical of cancer lesions. Automatic or semi-automatic methods are especially useful with this technique, due to the high quantity of data, in the form of 4D images (3D space + time), to be analysed in each test. This survey describes approaches to fully automatic computer-aided detection/diagnosis of breast lesions with CE-MRI, with particular emphasis on computational intelligence techniques.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.people | FA Cardillo | it |
| dc.authority.people | F Masulli | it |
| dc.authority.people | S Rovetta | it |
| dc.collection.id.s | 71c7200a-7c5f-4e83-8d57-d3d2ba88f40d | * |
| dc.collection.name | 04.01 Contributo in Atti di convegno | * |
| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
| dc.contributor.appartenenza.mi | 918 | * |
| dc.date.accessioned | 2024/02/21 03:34:58 | - |
| dc.date.available | 2024/02/21 03:34:58 | - |
| dc.date.issued | 2017 | - |
| dc.description.abstracteng | Abstract--Breast cancer is the most frequently diagnosed non-skin cancer in women, the second leading cause of death among women. Early detection of a breast cancer is fundamental for ensuring high survival rate. Imaging techniques are used to identify suspicious modifications of breast tissue. Among these, contrast-enhanced magnetic resonance imaging (CE-MRI) is particularly interesting for its lack of exposure to radiation and its ability to highlight differences in vascularisation, typical of cancer lesions. Automatic or semi-automatic methods are especially useful with this technique, due to the high quantity of data, in the form of 4D images (3D space + time), to be analysed in each test. This survey describes approaches to fully automatic computer-aided detection/diagnosis of breast lesions with CE-MRI, with particular emphasis on computational intelligence techniques. | - |
| dc.description.affiliations | Istituto di Linguistica Computazionale, CNR, Pisa, Italia. DIBRIS, Università di Genova, Italia DIBRIS, Università di Genova, Italia | - |
| dc.description.allpeople | F.A. Cardillo; F. Masulli; S. Rovetta | - |
| dc.description.allpeopleoriginal | F.A. Cardillo, F. Masulli, S. Rovetta | - |
| dc.description.fulltext | none | en |
| dc.description.numberofauthors | 1 | - |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/339525 | - |
| dc.language.iso | eng | - |
| dc.relation.conferencedate | 21-23/06/2017 | - |
| dc.relation.conferencename | 10th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom-2017) | - |
| dc.relation.conferenceplace | United Kingdom | - |
| dc.subject.keywords | medical image analysis | - |
| dc.subject.keywords | computational intelligence | - |
| dc.subject.singlekeyword | medical image analysis | * |
| dc.subject.singlekeyword | computational intelligence | * |
| dc.title | Automatic Approaches for CE-MRI Examination of the Breast: A Survey | en |
| dc.type.driver | info:eu-repo/semantics/conferenceObject | - |
| dc.type.full | 04 Contributo in convegno::04.01 Contributo in Atti di convegno | it |
| dc.type.miur | 273 | - |
| dc.type.referee | Sì, ma tipo non specificato | - |
| dc.ugov.descaux1 | 382552 | - |
| iris.orcid.lastModifiedDate | 2024/02/22 18:21:58 | * |
| iris.orcid.lastModifiedMillisecond | 1708622518579 | * |
| iris.sitodocente.maxattempts | 1 | - |
| Appare nelle tipologie: | 04.01 Contributo in Atti di convegno | |
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