Computer Vision News - April 2019

22 Women in Science Computer Vision News Women in Science As much as we love the sponsorships and the support of sponsors, we encourage them to participate in the conference and interact with the students. Other sponsors such as Intuitive also participate in IPCAI in addition to sponsorship and we are looking forward to more of this in 2019. I think, overall, it improves the quality of the conference. We are also interested in seeing more of scientific paper submissions from industry. I realize that not all of the companies in this area focus on research. They often concentrate on developing products and sales and service but if they have research, it would be very nice to have that presented as well. It would be helpful for the community to realize how we can better impact patient care if industry is with us. Do you have a message for PhD students? What would you like to tell those students who have difficulties or experience crisis and sometimes doubt about themselves? What is your best advice to succeed? I would say that if you ask any single one of us, we would say, “I have been there. I have gone through similar experiences.” First, reach out for support. Do not go into crisis alone. Your supervisors, your colleagues, your department, your university, they all share a purpose: to help you succeed in your studies. You are not alone. Every other student has most likely either gone through similar things or will go through similar things. For PhD, in particular, it is a long run. There are many unknowns. Otherwise, it wouldn’t be research. I would say, you have to be persistent. You have to focus on the end goal. Think about why you came here. What was your motivation to start your PhD? That is always a good support. Reach out. In conferences, look for people to mentor you. If you feel like you are lacking some aspect of mentorship, reach out to your supervisor. Reach out to your colleagues and other faculty members. It will help. Would you like to tell us more about your research? I want to talk about some of our deep learning methods and our machine learning approaches that have helped us guide interventions for prostate cancer. Prostate cancer is a prevalent health issue in the western world. In many countries in the western world, it’s either the second or the third cause of cancer-related death in men. The key here is to be able to accurately diagnosis high-grade cancer. Right now, ultrasound is used to guide biopsies for the prostate. The final diagnosis is made based on the analysis of the biopsy. What usually happens is that ultrasound is blind to the existence of cancer. It’s only used to guide the needle “We are also interested in seeing more of scientific paper submissions from industry”