First authored paper accepted to the IEEE International Symposium on Biomedical Imaging (ISBI’20).
MRI, an extremely expensive imaging modality, often suffers from a high degree of motion artifacting, which nececitates the need for some automated detection mechanism to avoid the inefficient use of high-cost radiologist labor to filter images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.
J. J. Ma et al., “Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges,” 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA, 2020, pp. 337-340, doi: 10.1109/ISBI45749.2020.9098735.