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Improving the breast cancer diagnosis using dig...

CETA-Ciemat
March 22, 2013
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Improving the breast cancer diagnosis using digital repositories

CETA-Ciemat

March 22, 2013
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  1. breast cancer diagnosis using digital repositories improving the César Suárez

    Ortega [email protected] 18-20 March 2013 International Work-Conference on Bioinformatics and Biomedical Engineering
  2. introduction » IMED is a collaboration among: » CETA-CIEMAT (Trujillo,

    Spain) » INEGI (Porto, Portugal) » FMUP-HSJ (Porto, Portugal) » Since 2008 » Created to ease the cancer diagnosis. » Focused on breast cancer.
  3. Breast cancer is most common cancer for women. According to

    World Health Organization: http://www.who.int/cancer/events/breast_cancer_month/en/ 1 1 1
  4. Double checking of cases reduces the ratio of missed cancer

    R. Ramos-Pollan, et al., Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis. Journal of Medical Systems (9 April 2011), pp. 1-11. 1 1
  5. what are CAD systems? » Assistance in the diagnosis of

    medical images » Multiple disciplines are combined: » Artificial intelligence » Digital image processing » Radiology
  6. what are CAD systems? » Assistance in the diagnosis of

    medical images » Multiple disciplines are combined: » Artificial intelligence » Digital image processing » Radiology They don't replace doctors, they support them.
  7. MIWAD BCDR MLCs three pillars Breast Cancer Digital Repository Machine

    Learning Classifiers Mammography Image Workstation for Analysis and Diagnosis
  8. MIWAD BCDR MLCs three pillars BCDR – A repository of

    annotated breast cancer cases. MLCs Machine learning classifiers for breast cancer diagnosis MIWAD – A workstation prototype with a CAD system.
  9. MLCs » Artificial intelligent systems. » They learn from data.

    » Lot of them! Decision Tree Association Rule
  10. MLCs » Artificial intelligent systems. » They learn from data.

    » Lot of them! Decision Tree Association Rule Neural Networks
  11. MLCs » Artificial intelligent systems. » They learn from data.

    » Lot of them! Decision Tree Association Rule Neural Networks Genetic Programming
  12. MLCs » Artificial intelligent systems. » They learn from data.

    » Lot of them! Decision Tree Association Rule Neural Networks Genetic Programming Support Vector Machines
  13. MLCs » Artificial intelligent systems. » They learn from data.

    » Lot of them! Decision Tree Association Rule Neural Networks Genetic Programming Support Vector Machines Clustering
  14. MLCs » Artificial intelligent systems. » They learn from data.

    » Lot of them! Decision Tree Association Rule Neural Networks Genetic Programming Support Vector Machines Clustering Bayesian Networks
  15. MLCs » Artificial intelligent systems. » They learn from data.

    » Lot of them! Decision Tree Association Rule Neural Networks Genetic Programming Support Vector Machines Clustering Bayesian Networks Inductive Logic
  16. MLCs » Artificial intelligent systems. » They learn from data.

    » Lot of them! Decision Tree Association Rule Neural Networks Genetic Programming Support Vector Machines Clustering Bayesian Networks Inductive Logic Sparse Dictionary
  17. current results » GRID and HPC to speed up the

    research. » Metrics » ROC → True positives VS. False positives » AUC → Probability of a true positive » Our classifiers?
  18. current results » GRID and HPC to speed up the

    research. » Metrics » ROC → True positives VS. False positives » AUC → Probability of a true positive » Our classifiers? AUC = 85%
  19. BCDR » Our reference dataset for MLCs. » It contains

    a custom subset of DICOM and: » Reviewed by specialists and biopsy proven » Cases classified using BI-RADS » Useful for researchers. 23 17 per lesion region intesity, texture and shape features clinical features per case
  20. two flavours BCDR-DMR 600 cases 828 segmented lesions 2837 mammographies

    3328x4084 TIFF images (14bpp) + 2073 ultrasound images 800x600 (8bpp)
  21. MIWAD » A workstation for analysing and diagnosing. » It's

    multiplatform (Windows, Linux & Mac) » Composed by two connected apps: MIWAD-DB MIWAD-CAD
  22. MIWAD-DB » It manages... » ...the clinical data » ...the

    mammographies » BCDR as data source. » It operates over BCDR data. » Executes MLCs with a 1-click process.
  23. MIWAD-CAD » Screening tool (based on TUDOR Viewer) » It

    includes image processing tools. » Tools to help in the segmentation process. » Three functions: 1 It stores the segmentations in BCDR. 2 It calculates image features. 3 It helps doctors to find lessions.
  24. MIWAD BCDR MLCs BCDR – A repository of annotated breast

    cancer cases. MLC Machine learning classifiers for breast cancer diagnosis MIWAD – A workstation prototype with a CAD system.
  25. future work » IMED goes educational! » MIWAD as a

    training app for radiologists: » Web architecture & responsive design. » Auto-evaluation using MLCs. » BCDR as a formative resource. » Continuation of the improvement of MLCs. (+90% AUC) » Certification for MLCs » Continuation of the BCDR growth (searching new sources)
  26. conclusions » CAD systems can achieve great results in: »

    Improving the breast cancer diagnosis » Training specialists » Specialists demand better educational apps (difficult to find) » All is about data! » It's difficult to get reliable data. » BCDR is the most complete open breast cancer repository. » MLCs data
  27. CETA-CIEMAT acknowledges the support received from the European Regional Development

    Fund through its Operational Programme Knowledge-based Economy.