STUDYING THE RELEVANCE OF BREAST IMAGING FEATURES

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Pedro Ferreira (Author) Other; Ines Dutra (Author) FCUP; Nano A Fonseca (Author) FCUP; Ryan Woods (Author) Other; Elizabeth Burnside (Author) Other

Conference proceedings International

Title: HEALTHINF 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON HEALTH INFORMATICS

Pages: 337-342

4th International Conference on Health Informatics (HEALTHINF 2011)

Rome, ITALY, JAN 26-29, 2011

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 4 Citations

Scientific classification

FOS: Medical and Health sciences > Health sciences

Other information

Authenticus ID: P-002-YN9

Abstract (EN): Breast screening is the regular examination of a woman's breasts to find breast cancer in an initial stage. The sole exam approved for this purpose is mammography that, despite the existence of more advanced technologies, is considered the cheapest and most efficient method to detect cancer in a preclinical stage. We investigate, using machine learning techniques, how attributes obtained from mammographies can relate to malignancy. In particular, this study focus is on how mass density can influence malignancy from a data set of 348 patients containing, among other information, results of biopsies. To this end, we applied different learning algorithms on the data set using the WEKA tools, and performed significance tests on the results. The conclusions are threefold: (1) automatic classification of a mammography can reach equal or better results than the ones annotated by specialists, which can help doctors to quickly concentrate on some specific mammogram for a more thorough study; (2) mass density seems to be a good indicator of malignancy, as previous studies suggested; (3) we can obtain classifiers that can predict mass density with a quality as good as the specialist blind to biopsy.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6