SmartMammaCAD
Intelligent Automated System for detecting Diagnostically Challenging Breast Cancers
Total Cost €
0EC-Contrib. €
0Partnership
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Explore the words cloud of the SmartMammaCAD project. It provides you a very rough idea of what is the project "SmartMammaCAD" about.
Project "SmartMammaCAD" data sheet
The following table provides information about the project.
| Coordinator |
UNIVERSIDAD DE GRANADA
Organization address contact info |
| 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.
| # | ||||
|---|---|---|---|---|
| 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 |
|---|---|---|---|
| 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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