Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

Autor:	Ma, Jeffrey, Nakarmi, Ukash, Kin, Cedric Yue Sik, Sandino, Christopher, Cheng, Joseph Y., Syed, Ali B., Wei, Peter, Pauly, John M., Vasanawala, Shreyas
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein. Comment: 4 pages, 8 Figures, Conference Submission
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.02907 Zobrazit plný text záznamu View this record from Arxiv