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SmartMammaCAD

Intelligent Automated System for detecting Diagnostically Challenging Breast Cancers

Total Cost €

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EC-Contrib. €

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Partnership

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Project "SmartMammaCAD" data sheet

The following table provides information about the project.

Coordinator
UNIVERSIDAD DE GRANADA 

Organization address
address: CUESTA DEL HOSPICIO SN
city: GRANADA
postcode: 18071
website: www.ugr.es

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Project website https://www.researchgate.net/project/Intelligent-Automated-System-for-Detecting-Diagnostically-Challenging-Breast-Cancers
 Total cost 257˙191 €
 EC max contribution 257˙191 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-GF
 Starting year 2015
 Duration (year-month-day) from 2015-09-01   to  2018-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSIDAD DE GRANADA ES (GRANADA) coordinator 257˙191.00
2    Florida State University US (Tallahassee) partner 0.00

Map

 Project objective

In this project, Dr. Ignacio Alvarez Illan proposes to develop a novel automated diagnosis system that supports the radiologist in the breast cancer diagnosis in Dynamic Contrast Enhance-Magnetic Resonance Imaging (DCE-MRI) by including critical components of the radiological work-flow such as motion compensation, segmentation and diagnosis of breast tumours. The expected results of this interdisciplinary project will definitely have applications and impact in the European society and its health and the overarching goals of the '2020 Vision for the European Research Area’. Specifically, improving diagnosis of major diseases such as breast cancer is a research priority in the European Union.

The main goal and overall objective of this project is to develop computer aided diagnosis (CAD) methods, and image processing techniques to improve diagnostic accuracy and efficiency of cancerrelated breast lesions. Non-mass-enhancing lesions exhibit a heterogeneous appearance in breast MRI with high variations in kinetic characteristics and typical morphological parameters, and have a specificity and sensitivity much lower than mass-enhancing lesions. For this reason, new segmentation algorithms and kinetic parameters can be potentially used as an alternative to the methods for mass-enhanced lesions.

To develop and implement CAD methods and image processing techniques, three different research objectives are presented in this project. They include basic research, strategic research, applied research and transfer of knowledge: i) Develop non-rigid registration and segmentation techniques to incorporate spatial variations in temporal enhancement. ii) Develop kinetic feature descriptors to quantify significant differences between the benign and malignant lesions. iii) Develop and validate algorithms, interfaces and software implementation for real applications of CAD of breast cancer.

 Publications

year authors and title journal last update
List of publications.
2018 Ignacio Alvarez Illan, Javier Ramirez, J. M. Gorriz, Maria Adele Marino, Daly Avendano, Thomas Helbich, Pascal Baltzer, Katja Pinker, Anke Meyer-Baese
Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging
published pages: 1-11, ISSN: 1555-4309, DOI: 10.1155/2018/5308517
Contrast Media & Molecular Imaging 2018 2019-07-23
2017 J. M. Gorriz, J. Ramirez, J. Suckling, Ignacio Alvarez Illan, Andres Ortiz, F. J. Martinez-Murcia, Fermin Segovia, D. Salas-Gonzalez, Shuihua Wang
Case-Based Statistical Learning: A Non-Parametric Implementation With a Conditional-Error Rate SVM
published pages: 11468-11478, ISSN: 2169-3536, DOI: 10.1109/ACCESS.2017.2714579
IEEE Access 5 2019-07-23
2017 I. Alvarez Illan, A. Meyer-Baese, J. Perez Matos, M. B. I. Lobbes, K. Pinker
Machine learning for challenging tumour detection and classification in breast cancer
published pages: , ISSN: , DOI: 10.1594/ecr2017/C-3151
epos 2019-07-23
2017 Juan M. Gorriz, Javier Ramirez, John Suckling, F.J. Martinez-Murcia, I.A. Illán, F. Segovia, A. Ortiz, D. Salas-González, D. Castillo-Barnés, C.G. Puntonet
A semi-supervised learning approach for model selection based on class-hypothesis testing
published pages: 40-49, ISSN: 0957-4174, DOI: 10.1016/j.eswa.2017.08.006
Expert Systems with Applications 90 2019-07-23

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The information about "SMARTMAMMACAD" are provided by the European Opendata Portal: CORDIS opendata.

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