Computer Vision News - May 2022

30 BEST PAPER ISBI 2022 artifacts can reduce the quality of images when scanning at low doses. This work proposes using semi-supervised learning to overcome this, with a solution based on deep learning , developed using Python and the PyTorch library. Vatsala explains that the work had many different parts, and to make it more robust, the team each worked individually on separate sections before trying to make the whole thing fit together. Did they succeed the first time? “ Definitely not on the first try! ” she laughs. “ When you’re focusing on the bigger picture, sometimes you miss simple things like small errors in your script, and then after speaking to your co-authors, you realize you’ve made a silly mistake. For example, I got an order number error in my code, and I couldn’t understand why, but it was simply because of the way I was calculating a probability value. There was Patients are subject to radiation during the scanning process for both PET and CT . If someone needs multiple scans over their life, this can be detrimental to their health. Using low-dose PET-CT imaging reduces the health risks associated with radiation. However, noise and other undesirable Vatsala Sharma is a third-year PhD student at the Indian Institute of Technology in Bombay. Vatsala and her colleagues have just won the Best Paper Award at ISBI 2022 for their work “Semi-Supervised Deep Expectation-Maximization for Low-Dose PET-CT”, proposing a new system for low-dose PET-CT image enhancement in the case of limited data availability. Vatsala speaks to us about their acclaimed research. SEMI-SUPERVISED DEEP EXPECTATION-MAXIMIZATION FOR LOW-DOSE PET-CT