Computer Vision News - June 2020

3 Summary WEISS at UCL (part 2) 29 paper has been written. Like many other fields, the application of deep learning has revolutionized medical image computing, and it started early. “I think it’s been about five years now,” Yipeng says. “One of the reasons is because we are very close to computer vision. Surgery and interventional applications are usually at a later stage of the curve in terms of adopting new methodology, but it’s starting now. The advantage is everything can be done very quickly in real-time, which is essential during surgery.” However, there are challenges. Data is a key requirement for deep learning algorithms to work, but for ultrasound, data availability can be limited. Also, most applications involve heavy human interaction, whereas in computer vision, you work with a picture or video. “ I personally see there’s a traditional image- guided interventional subfield and a robotic field,” Yipeng explains. “The robotic field is assuming robots will be controlling all medical devices in the future. With that as our end goal, we try to make our algorithms as automated as possible. We’re still very close, but these two fields will merge at some point.”