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Full-Text Articles in Medicine and Health Sciences
When Worlds Collide: Boundary Management Of Adolescent And Young Adult Childhood Cancer Survivors And Caregivers, Elizabeth A. Ankrah, Arpita Bhattacharya, Lissamarie Donjuan, Franceli L. Cibrian, Anamara Ritt-Olson, Joel Milam, Lilibeth Torno, Gillian R. Hayes
When Worlds Collide: Boundary Management Of Adolescent And Young Adult Childhood Cancer Survivors And Caregivers, Elizabeth A. Ankrah, Arpita Bhattacharya, Lissamarie Donjuan, Franceli L. Cibrian, Anamara Ritt-Olson, Joel Milam, Lilibeth Torno, Gillian R. Hayes
Engineering Faculty Articles and Research
Adolescent and young adult childhood cancer survivors experience health complications, late or long-term biomedical complications, as well as economic and psychosocial challenges that can have a lifelong impact on their quality-of-life. As childhood cancer survivors transition into adulthood, they must learn to balance their identity development with demands of everyday life and the near- and long-term consequences of their cancer experience, all of which have implications for the ways they use existing technologies and the design of novel technologies. In this study, we interviewed 24 childhood cancer survivors and six caregivers about their cancer survivorship experiences. The results of our …
Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen
Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen
Engineering Faculty Articles and Research
Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …