Computer-aided detection; the effect of training databases on detection of subtle breast masses.

Academic Radiology
Bin ZhengDavid Gur

Abstract

Lesion conspicuity is typically highly correlated with visual difficulty for lesion detection, and computer-aided detection (CAD) has been widely used as a "second reader" in mammography. Hence, increasing CAD sensitivity in detecting subtle cancers without increasing false-positive rates is important. The aim of this study was to investigate the effect of training database case selection on CAD performance in detecting low-conspicuity breast masses. A full-field digital mammographic image database that included 525 cases depicting malignant masses was randomly partitioned into three subsets. A CAD scheme was applied to detect all initially suspected mass regions and compute region conspicuity. Training samples were iteratively selected from two of the subsets. Four types of training data sets-(1) one including all available true-positive mass regions in the two subsets ("all"), (2) one including 350 randomly selected mass regions ("diverse"), (3) one including 350 high-conspicuity mass regions ("easy"), and (4) one including 350 low-conspicuity mass regions ("difficult")-were assembled. In each training data set, the same number of randomly selected false-positive regions as the true-positives were also included. Two classifie...Continue Reading

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Citations

Mar 26, 2014·International Journal of Computer Assisted Radiology and Surgery·Maxine TanBin Zheng
Aug 9, 2016·Medical Image Analysis·Thijs KooiNico Karssemeijer
Apr 19, 2018·Physics in Medicine and Biology·Seyedehnafiseh MirniaharikandeheiBin Zheng

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