Matching Items (4)
Description
Stafford Leak Warren studied nuclear medicine in the United States during the twentieth century. He used radiation to make images of the body for diagnosis or treatment and developed the mammogram, a breast imaging technique that uses low-energy X-rays to produce an image of breasts. Mammograms allow doctors to diagnose breast cancer in its early and most treatable stages. Warren was also a medical advisor to the Manhattan Project, the US government’s program to develop an atomic bomb during World War II, and he was responsible for the health and safety aspects of the Trinity Test, the first atomic bomb test in the US. Warren’s invention of the mammogram has allowed physicians to diagnose breast cancer in women during its most treatable stages, preventing deaths due to breast cancer.
ContributorsZhu, Meilin (Author) / Dhein, Kelle (Editor) / Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia. (Publisher) / Arizona Board of Regents (Publisher)
Created2017-08-30
Description
In this thesis, the applications of deep learning in the analysis, detection and classification of medical imaging datasets were studied, with a focus on datasets having a limited sample size. A combined machine learning-deep learning model was designed to classify one small dataset, prostate cancer provided by Mayo Clinic, Arizona. Deep learning model was implemented to extract imaging features followed by machine learning classifier for prostate cancer diagnosis. The results were compared against models trained on texture-based features, namely gray level co-occurrence matrix (GLCM) and Gabor. Some of the challenges of performing diagnosis on medical imaging datasets with limited sample sizes, have been identified. Lastly, a set of future works have been proposed. Keywords: Deep learning, radiology, transfer learning, convolutional neural network.
ContributorsSarkar, Suryadipto (Author) / Wu, Teresa (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Silva, Alvin (Committee member) / Arizona State University (Publisher)
Created2021
Description
This research evaluates the capabilities of typical radiological measures and dual-energy systems to differentiate common kidney stones materials: uric acid, oxalates, phosphates, struvite, and cystine. Two different X-ray spectra (80 kV and 120 kV) were applied and the dual-energy ratio of individual kidney stones was used to figure out the discriminability of different materials. A CT cross-section with a prospective kidney stone was analyzed to see the capabilities of such a technique. Typical radiological measures suggested that phosphates and oxalate stones can be distinguished from uric acid stones while dual-energy seemed to prove similar effectiveness.
ContributorsDelafuente, Nicholas William (Author) / Rez, Peter (Thesis director) / Alarcon, Ricardo (Committee member) / Department of Physics (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Rapid advancements in Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are widening the playing field for automated decision assistants in healthcare. The field of radiology offers a unique platform for this technology due to its repetitive work structure, ability to leverage large data sets, and high position for clinical and social impact. Several technologies in cancer screening, such as Computer Aided Detection (CAD), have broken the barrier of research into reality through successful outcomes with patient data (Morton, Whaley, Brandt, & Amrami, 2006; Patel et al, 2018). Technologies, such as the IBM Medical Sieve, are growing excitement with the potential for increased impact through the addition of medical record information ("Medical Sieve Radiology Grand Challenge", 2018). As the capabilities of automation increase and become a part of expert-decision-making jobs, however, the careful consideration of its integration into human systems is often overlooked. This paper aims to identify how healthcare professionals and system engineers implementing and interacting with automated decision-making aids in Radiology should take bureaucratic, legal, professional, and political accountability concerns into consideration. This Accountability Framework is modeled after Romzek and Dubnick’s (1987) public administration framework and expanded on through an analysis of literature on accountability definitions and examples in military, healthcare, and research sectors. A cohesive understanding of this framework and the human concerns it raises helps drive the questions that, if fully addressed, create the potential for a successful integration and adoption of AI in radiology and ultimately the care environment.
ContributorsGilmore, Emily Anne (Author) / Chiou, Erin (Thesis director) / Wu, Teresa (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05