I presented our work titled “Machine-to-Machine Transfer Function: Transferring Deep Learning Models between Ultrasound Machines”

I presented our solution for the acquisition-related data mismatch problem in ultrasound imaging in IUS2022.

Deep learning (DL) can fail when there are data mismatches between training and testing data distributions. Due to its operator-dependent nature, acquisition-related data mismatches, caused by different scanner settings, can occur in ultrasound imaging. Therefore,…

I presented our work on calibrating acquisition-related data mismatches in Gordon Research Conferences: Here is the poster in pptx:

I will present our work on calibrating acquisition-related data mismatches in UITC Symposium. Here is the program details:

Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL…

Ultrasound Localization Microscopy (ULM) offers a cost-effective modality for microvascular imaging by using intravascular contrast agents (microbubbles). However, ULM has a fundamental trade-off between acquisition time and spatial resolution, which makes clinical translation challenging. In…

Simulation Scheme: There are 128 sensors evenly spaced. Width of each sensor is 0.27 mm and empty space between each sensor is 0.03 mm. We assume that there is no attenuation. Image has 50 mm…