Parametric Transfer Learning on Prostate and Breast Cancer

dc.contributor.advisorAgaian, Sos
dc.contributor.advisorGrigoryan, Artyom
dc.contributor.authorSathyanarayana, Shilpana
dc.contributor.committeeMemberAkopian, David
dc.contributor.committeeMemberRad, Paul
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractIntricate structure is analyzed by deep learning algorithms by extracting hierarchical feature representations. Employing deep learning algorithms for such computer-aided classification and detection has been exhaustively analyzed and has proven to out-perform human detection from many perspectives. Heterogeneous transfer learning algorithms learn features by cross-mapping between feature spaces by estimating millions of parameters. The convolutional neural network model analysis that we perform uses a cross-domain transfer learning approach to learn the feature-spaces from one source domain and map correlated feature instance correspondences in the target domain with noted transitions from generality to specificity of the neurons from one domain to the other, across the layers of the deep neural network. In this work, we exploit methods of transferring parameters across layers and analyzing the correlation between the domains to extract features to develop computer-aided classification and detection systems. A method of transferring parameters learnt from one feature space of a correlated medical domain to support the learning of the parameters from another feature space of the same medical domain is performed in this work.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent79 pages
dc.subjectBreast cancer histopathology
dc.subjectConvolutional neural network
dc.subjectDeep learning
dc.subjectPrincipal component analysis
dc.subjectProstate cancer frozen section
dc.subjectTransfer learning
dc.titleParametric Transfer Learning on Prostate and Breast Cancer
dcterms.accessRightspq_closed and Computer Engineering of Texas at San Antonio of Science


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