Computer Vision News - May 2022

36 Exclusive Interview coming up to determine if any alternative treatments could work. Compared to other cancers which have moved towards personalized treatments, brain tumor patients get standard chemoradiation. That’s not changed in the last 25 years. Our group has been developing tools and AI and machine learning algorithms to begin to move the needle towards personalized medicine. The aim is to identify patients who might be more suitable for a specific treatment instead of giving them the same aggressive dose of chemoradiation that may be unnecessary. How is the machine learning community getting involved? The technical community is recognizing these challenges more and more. A prominent example of that is the BraTS challenge through MICCAI, which has been running for ten years now. Through BraTS and other ongoing brain tumor challenges, more data is being made available, which hugelybenefitsour communityandhelpsus answer these more challenging questions. accuracy for distinguishing between a benign condition or tumor recurrence. By comparison, for neuroradiologists, the accuracy is close to a coin toss. How did you end up working with the brain? My master’s and PhD work at Rutgers was focused on prostate cancer, but I’ve always been fascinated by the brain. My cousin has been suffering from multiple sclerosis for several years. That was another reason for me to delve more into neurological disorders. Towards the end of my PhD, I started talking to a neurosurgeon, who told me how limited the options for brain tumor patients were. That resonated with me, so right after my PhD, I started working in this field. How good is the community today at tackling brain tumors? The clinical community still has many questions that need to be addressed. There have been many clinical trials but not much success so far. Multiple trials are