Computer Vision News - May 2022

37 Pallavi Tiwari are trying to capture this heterogeneity to predict outcomes in treatment-naive patients and patients after treatment. Are you able to predict malignancy and the growth patterns of tumors? When we think about a brain tumor, it’s like a ball sitting in the patient’s head which is expanding. This ball impacts the rest of the brain in what is known as mass effect. If the tumor grows quickly, this mass effect will impact the rest of the brain. We’ve developed a feature looking at the periphery of the tumor and the region outside it. It captures this mass effect on imaging and uses it to predict how long the patient will live. We compute it by measuring biophysical deformations on How fast do advancements translate into practical applications in real life? A big challenge is that groups are working on these problems in a silo. Multiple people must come together both in terms of datasets and algorithms for us to start translating this work into clinical practice. We need a shared resource where we can test each other’s algorithms. To some extent, that’s started to happen now with federated learning and Spyros Bakas’s work at the University of Pennsylvania. Are brain tumors different for you as a medical imaging expert fromother lesions that the human body may have? One thing that’s challenging about brain tumors isthat they’rehighlyheterogeneous. We presented a paper at MICCAI in 2014 and then published a journal paper in 2016 about a CoLlAGe feature descriptor that captures this intra-tumoral heterogeneity locally. The techniques we’re developing “We need a shared resource where we can test each other’s algorithms.” Brain Imaging Computing lab (directed by Dr. Tiwari) in 2019 before the COVID-19 pandemic.