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Knowledge‐based computer‐aided detection of masses on digitized mammograms: A preliminary assessment
Author(s) -
Chang YuanHsiang,
Hardesty Lara A.,
Hakim Christiane M.,
Chang Thomas S.,
Zheng Bin,
Good Walter F.,
Gur David
Publication year - 2001
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1359250
Subject(s) - receiver operating characteristic , computer science , cad , pruning , data mining , pattern recognition (psychology) , sensitivity (control systems) , artificial intelligence , identification (biology) , computer aided diagnosis , similarity (geometry) , set (abstract data type) , machine learning , image (mathematics) , botany , engineering drawing , electronic engineering , agronomy , biology , programming language , engineering
The purpose of this work was to develop and evaluate a computer‐aided detection (CAD) scheme for the improvement of mass identification on digitized mammograms using a knowledge‐based approach. Three hundred pathologically verified masses and 300 negative, but suspicious, regions, as initially identified by a rule‐based CAD scheme, were randomly selected from a large clinical database for development purposes. In addition, 500 different positive and 500 negative regions were used to test the scheme. This suspicious region pruning scheme includes a learning process to establish a knowledge base that is then used to determine whether a previously identified suspicious region is likely to depict a true mass. This is accomplished by quantitatively characterizing the set of known masses, measuring “similarity” between a suspicious region and a “known” mass, then deriving a composite “likelihood” measure based on all “known” masses to determine the state of the suspicious region. To assess the performance of this method, receiver‐operating characteristic (ROC) analyses were employed. Using a leave‐one‐out validation method with the development set of 600 regions, the knowledge‐based CAD scheme achieved an area under the ROC curve of 0.83. Fifty‐one percent of the previously identified false‐positive regions were eliminated, while maintaining 90% sensitivity. During testing of the 1000 independent regions, an area under the ROC curve as high as 0.80 was achieved. Knowledge‐based approaches can yield a significant reduction in false‐positive detections while maintaining reasonable sensitivity. This approach has the potential of improving the performance of other rule‐based CAD schemes.