title: Patterns out of cases using kohonen maps in breast cancer diagnosis.
abstract: DESMAI is a framework for helping experts in breast cancer diagnosis. It allows experts to explore digital mammographic image databases according to a certain topology criteria when they need to decide whether a sample is benign or malignant. In this way, they are provided with complementary information to enhance their interpretations and predictions. The core of the application is a SOMCBR system, which is variant of a Case-Based Reasoning system featured by organizing the case memory using a Self-Organizing Map. The article presents a strategy for improving the SOMCBR reliability thanks to the relations between cases and clusters. The approach is successfully applied in DESMAI for estimating, if it is possible, the class of the recovered mammographies.
authors.affiliation: Grup de Recerca en Sistemes Intelligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Spain. afornells@salle.url.edu
authors.forename: A
authors.initials: A
authors.lastname: Fornells
authors.forename: J M
authors.initials: JM
authors.lastname: Martorell
authors.forename: E
authors.initials: E
authors.lastname: Golobardes
authors.forename: J M
authors.initials: JM
authors.lastname: Garrell
authors.forename: X
authors.initials: X
authors.lastname: Vilasís
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D016000:Cluster Analysis; D019992:Databases as Topic; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008327:Mammography / Q000379:methods; D016571:Neural Networks, Computer; D015203:Reproducibility of Results