[Reading screening mammograms with the help of neural networks].
With digital mammography it is possible to assist radiologists in breast cancer screening with computers to improve their reading performance. The need for this has been demonstrated by studies showing a large variability in skill of radiologists reading mammograms. Moreover, retrospective studies show that a significant number of cancers are clearly visible on earlier screening mammograms, even for 'trained' computers. Methods for automated detection of breast cancer in mammograms often use artificial neural networks. These are 'trained' to recognize abnormal mammographic areas using a large database of known cases. For detection of microcalcification clusters very reliable algorithms exist, with such high sensitivity that radiologists can limit their search to areas that have been marked 'suspect' by the computer. The development of methods to recognize malignant masses is much more difficult, but ample progress has been achieved in recent years.
Karssemeijer
N
N
Academisch Ziekenhuis, afd. Radiodiagnostiek, Nijmegen.
Veldkamp
W J
WJ
te Brake
G M
GM
Hendriks
J H
JH
D000328:Adult; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008327:Mammography / Q000191:economics / Q000639:trends*; D008403:Mass Screening / Q000191:economics / Q000379:methods*; D009426:Netherlands; D016571:Neural Networks, Computer; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
Beoordeling van screeningsmammogrammen met behulp van neurale netwerken.