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dc.contributor.authorMakeev, Andrey
dc.contributor.authorToner, Brian
dc.contributor.authorQian, Marian
dc.contributor.authorBadal, Andreu
dc.contributor.authorGlick, Stephen J
dc.date.accessioned2021-07-27T21:54:47Z
dc.date.available2021-07-27T21:54:47Z
dc.date.issued2021-05-29
dc.identifier.citationMakeev, A., Toner, B., Qian, M., Badal, A., & Glick, S. J. (2021). Using convolutional neural networks to discriminate between cysts and masses in Monte Carlo-simulated dual-energy mammography. Medical Physics.en_US
dc.identifier.issn0094-2405
dc.identifier.pmid34050965
dc.identifier.doi10.1002/mp.15005
dc.identifier.urihttp://hdl.handle.net/10150/661019
dc.description.abstractPurpose: A substantial percentage of recalls (up to 20%) in screening mammography is attributed to extended round lesions. Benign fluid-filled breast cysts often appear similar to solid tumors in conventional mammograms. Spectral imaging (dual-energy or photon-counting mammography) has been shown to discriminate between cysts and solid masses with clinically acceptable accuracy. This work explores the feasibility of using convolutional neural networks (CNNs) for this task. Methods: A series of Monte Carlo experiments was conducted with digital breast phantoms and embedded synthetic lesions to produce realistic dual-energy images of both lesion types. We considered such factors as nonuniform anthropomorphic background, size of the mass, breast compression thickness, and variability in lesion x-ray attenuation. These data then were used to train a deep neural network (ResNet-18) to learn the differences in x-ray attenuation of cysts and masses. Results: Our simulation results showed that the CNN-based classifier could reliably discriminate between cystic and solid mass round lesions in dual-energy images with an area under the receiver operating characteristic curve (ROC AUC) of 0.98 or greater. Conclusions: The proposed approach showed promising performance and ease of implementation, and could be applied to novel photon-counting detector-based spectral mammography systems. © 2021 American Association of Physicists in Medicine. This article has been contributed to by US Government employees and their work is in the public domain in the USA.en_US
dc.language.isoenen_US
dc.publisherJohn Wiley and Sons Ltden_US
dc.rights© 2021 American Association of Physicists in Medicine. This article has been contributed to by US Government employees and their work is in the public domain in the USA.en_US
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/en_US
dc.subjectMonte Carlo Simulationen_US
dc.subjectbreast cystsen_US
dc.subjectneural networken_US
dc.subjectsolid massesen_US
dc.subjectspectral mammographyen_US
dc.titleUsing convolutional neural networks to discriminate between cysts and masses in Monte Carlo-simulated dual-energy mammographyen_US
dc.typeArticleen_US
dc.identifier.eissn2473-4209
dc.contributor.departmentProgram in Applied Mathematics, University of Arizonaen_US
dc.identifier.journalMedical physicsen_US
dc.description.notePublic domain articleen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleMedical physics
refterms.dateFOA2021-07-27T21:54:47Z
dc.source.countryUnited States


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© 2021 American Association of Physicists in Medicine. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
Except where otherwise noted, this item's license is described as © 2021 American Association of Physicists in Medicine. This article has been contributed to by US Government employees and their work is in the public domain in the USA.