Date of Award
Open Access Master's Thesis
Master of Science in Cybersecurity (MS)
Administrative Home Department
Department of Computer Science
Committee Member 1
Committee Member 2
Within a given enterprise network, an array of data types needs to be communicated. These network transmissions consist of images, videos, text, and binaries that have unique requirements of bandwidth and computational overhead to transmit. With respect to medical informatics, these include a multitude of varying subjects, standards, and modalities which are communicated to and from imaging equipment, clinicians, and medical archives. To reduce the required bandwidth to transmit, or provide adequate storage capacity for archival purposes, the data may be compressed in such a way that reduces the size of the image when it is transferred or stored. The original data may be reconstructed either completely or to an acceptable degree of completeness using lossy or lossless compression strategies. The scope of this inquiry is to define ways in which convolutional compressive autoencoders may be used for lossy compression. Multiple approaches will be identified and introduced to define their respective optimal datasets, along with their tuned hyperparameters.
Warren, Charles, "An Analysis of Compressive Convolutional Autoencoders for Image Archiving In Medical Informatics", Open Access Master's Thesis, Michigan Technological University, 2022.