Every scheduled treatment at a radiation therapy clinic involves a series of safety
protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion
an entirely preventable medical event, an accident, may occur. Delivering a treatment
plan to the wrong patient is preventable, yet still is a clinically documented error.
This research describes a computational method to identify patients with a novel
machine learning technique to combat misadministration.The patient identification
program stores face and fingerprint data for each patient. New, unlabeled data from
those patients are categorized according to the library. The categorization of data by
this face-fingerprint detector is accomplished with new machine learning algorithms
based on Sparse Modeling that have already begun transforming the foundation of
Computer Vision. Previous patient recognition software required special subroutines
for faces and di↵erent tailored subroutines for fingerprints. In this research, the same
exact model is used for both fingerprints and faces, without any additional subroutines
and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting,
demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling
is possible because natural images are inherrently sparse in some bases, due to their
inherrant structure. This research chooses datasets of face and fingerprint images to
test the patient identification model. The model stores the images of each dataset as
a basis (library). One image at a time is removed from the library, and is classified by
a sparse code in terms of the remaining library. The Locally Competetive Algorithm,
a truly neural inspired Artificial Neural Network, solves the computationally difficult
task of finding the sparse code for the test image. The components of the sparse
representation vector are summed by `1 pooling, and correct patient identification is
consistently achieved 100% over 1000 trials, when either the face data or fingerprint
data are implemented as a classification basis. The algorithm gets 100% classification
when faces and fingerprints are concatenated into multimodal datasets. This suggests
that 100% patient identification will be achievable in the clinal setting. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_33696 |
| Contributors | Lewkowitz, Stephanie (author), Kalantzis, Georgios (Thesis advisor), Florida Atlantic University (Degree grantor), Charles E. Schmidt College of Science, Department of Physics |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 76 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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