Breast cancer is one of the cancer type most diagnosed. Its causes are unknown so there is not an eective way to prevent it, which increases the mortality rate. The early detection of breast cancer is the best practice to reduce this rate. The double reading of mammograms is a common practice to reduce the rate of missed cancer, but it has a hight cost. Computer Aided-diagnosis (CADx) Systems and Machine Learning Classiers (MLCs) help to reduce these cost making automatic the second read of the mammograms. The collaboration between CETA-CIEMAT, INEGI and FMUP-HSJ has generated a set of valuable resources to improve the breast cancer diag- nosis process. We aim to achieve a reference repository for breast cancer diagnosis with BCDR, improving the existing implementations by storing a large number of annotated diagnosed cases reviewed by specialists, so researchers can have a reliable source of information for their researches. Using the BCDR data, the main MLCs algorithms are being tested in order to nd the best conguration for obtaining accurate automatic di- agnosis tool. MIWAD is a workstation which eases the specialist's job in their diagnoses. It is a rich client for BCDR, and oers
['Cesar Ortega', 'Jose Miguel Franco-Valiente', 'Manuel Rubio del Solar', 'Guillermo Díaz-Herrero', 'Miguel Ángel Guevara-López', 'Naimy González-de-Posada', 'Daniel C. Moura', 'Pedro Cunha', 'Isabel Ramos', 'Joana Loureiro', 'Bruno Ferreira-De-Araujo']
Improving the breast cancer diagnosis using digital repositories