Date of Graduation
Bachelor of Science in Computer Engineering
Computer Science and Computer Engineering
Committee Member/Second Reader
Modern deep learning architectures have become increasingly popular in medicine, especially for analyzing medical images. In some medical applications, deep learning image analysis models have been more accurate at predicting medical conditions than experts. Deep learning has also been effective for material analysis on photographs. We aim to leverage deep learning to perform material analysis on medical images. Because material datasets for medicine are scarce, we first introduce a texture dataset generation algorithm that automatically samples desired textures from annotated or unannotated medical images. Second, we use a novel Siamese neural network called D-CNN to predict patch similarity and build a distance metric between medical materials. Third, we apply and update a material analysis network from prior research, called MMAC-CNN, to predict materials in texture samples while also learning attributes that further separate the material space. In our experiments, we found that the MMAC-CNN is 89.5% accurate at predicting materials in texture patches, while also transferring knowledge of materials between image modalities.
Material analysis, radiography, computer vision, neural networks, bioinformatics
Molder, C. (2021). Using Deep Learning to Analyze Materials in Medical Images. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/89