Publications by authors named "Jheng-Ru Chen"
Article Synopsis
- * A total of 85 participants, both ambulatory and nonambulatory, had ultrasound scans of their gastrocnemius muscles analyzed using various deep learning models, with VGG-19 showing the best classification performance and accuracy.
- * The analysis utilized techniques like Grad-CAM to identify key ultrasound features important for evaluating walking ability in DMD patients, demonstrating the potential of this method for assessing the condition.
Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics.
View Article and Find Full Text PDFHigh-intensity focused ultrasound (HIFU) is a well-accepted tool for noninvasive thermal therapy. To control the quality of HIFU treatment, the focal spot generated in tissues must be localized. Ultrasound imaging can monitor heated regions; in particular, the change in backscattered energy (CBE) allows parametric imaging to visualize thermal information in the tissue.
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