Improving the performance of lesion-based computer-aided detection schemes of breast masses using a case-based adaptive cueing method

Maxine Tan, Faranak Aghaei, Yunzhi Wang, Wei Qian, Bin Zheng

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1 Citation (Scopus)


Current commercialized CAD schemes have high false-positive (FP) detection rates and also have high correlations in positive lesion detection with radiologists. Thus, we recently investigated a new approach to improve the efficacy of applying CAD to assist radiologists in reading and interpreting screening mammograms. Namely, we developed a new global feature based CAD approach/scheme that can cue the warning sign on the cases with high risk of being positive. In this study, we investigate the possibility of fusing global feature or case-based scores with the local or lesion-based CAD scores using an adaptive cueing method. We hypothesize that the information from the global feature extraction (features extracted from the whole breast regions) are different from and can provide supplementary information to the locally-extracted features (computed from the segmented lesion regions only). On a large and diverse full-field digital mammography (FFDM) testing dataset with 785 cases (347 negative and 438 cancer cases with masses only), we ran our lesion-based and case-based CAD schemes "as is" on the whole dataset. To assess the supplementary information provided by the global features, we used an adaptive cueing method to adaptively adjust the original CAD-generated detection scores (Sorg) of a detected suspicious mass region based on the computed case-based score (Scase) of the case associated with this detected region. Using the adaptive cueing method, better sensitivity results were obtained at lower FP rates (≤ 1 FP per image). Namely, increases of sensitivities (in the FROC curves) of up to 6.7% and 8.2% were obtained for the ROI and Case-based results, respectively.

Original languageEnglish
Title of host publicationMedical Imaging 2016
Subtitle of host publicationComputer-Aided Diagnosis
EditorsGeorgia D. Tourassi, Samuel G. Armato
PublisherSPIE - International Society for Optical Engineering
ISBN (Electronic)9781510600201
Publication statusPublished - 2016
Externally publishedYes
EventConference on Medical Imaging - Computer-Aided Diagnosis 2016 - San Diego, United States of America
Duration: 28 Feb 20162 Mar 2016 (Proceedings)

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceConference on Medical Imaging - Computer-Aided Diagnosis 2016
Country/TerritoryUnited States of America
CitySan Diego
Internet address


  • Adaptive cueing
  • Breast cancer
  • Computer-aided detection (CAD) of mammograms
  • Global feature (case-based) CAD schemes
  • Lesion-based CAD schemes

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