Gorkem Polat is with the Department of Health Informatics in Turkey (https://hi.metu.edu.tr/) and he has won the Endoscopic Artefact Detection Challenge 2020 (EAD2020), hosted by Dr. Sharib Ali. The EAD2020 will be held in conjunction with the IEEE International Symposium of Biomedical Imaging 2020 (ISBI 2020). As MedIAN likes to support early career researchers and extend their network, they sponsored a full travel grant for his participation in the ISBI2020. Gorkem Polat is a PhD student at Middle East Technical University (METU), Ankara, Turkey. Previously he obtained his M.Sc. and a B.Sc. in Electrical-Electronics Engineering from the same university. His research interests include deep learning, machine learning, computer vision, object detection and medical image processing. His Ph.D. thesis is concerned with the inflammatory bowel disease detection in colonoscopy images. His project is a joint effort of the Institute of Gastroenterology at Marmara University and Deep Learning and Computer Vision Lab at METU. 

We wanted to know more about how he felt about the challenge he had just participated in and how he felt about the online workshop and the winning the travel grant. 

1)   What do you think about EAD2020 challenge?

Detecting the artefacts in endoscopic images has significant effects on the clinical endoscopic procedures such as enhanced real-time visualization during diagnosis or more efficient post-processing. EAD2020 challenge continues to carry the flag it received from EAD2019. These challenges encourage the medical image analysis community to do even better because of the collaboration and exciting atmosphere.  In addition, it is very challenging to collect and form the dataset provided in EAD2020. I believe that both dataset and proposed methods in this challenge will shed light on future studies.

 2) What was exciting about participating in the online challenge of EAD2020?

My Ph.D. focuses on the image analysis of the colonoscopy images; therefore, it was a good chance for me to both compete and meet with researchers in the same domain. EAD2020 gathered many researchers from all over the world and it was a good experience for me to see my progress among the participants. In addition, presentations of the experts in this domain (both medical and engineering side) at the challenge workshop was very valuable because it is not always easy to meet and listen experts in the same field as the field we work in.

3) What did you learn and what opportunities did it give you?

First of all, competing in a challenge accelerates the learning process because in order to succeed, you have to learn fast and apply fast; therefore, it was a dense learning process for me, which was one of the most beneficial things for me in this challenge. Besides, generally, there are many tasks and experiments to perform; therefore, effective teamwork is critical. As Deep Learning and Computer Vision Lab in METU, we were a team of four people, and each person has a contribution to this success. Overall, working on a real-life problem and having meaningful results encouraged me more to study on medical imaging.

4) How do you feel regarding winning this challenge?

It is very honoring for me to win this challenge, where many researchers around the world have participated. This kind of success always encourages me to accomplish more; therefore, now, I am more motivated to work on the more challenging and bigger problems.

 5) What do you think AI challenges like this can do to transform health care and help you understand the challenges and how can this be used for care pathways in the future?

Medical images are sourced from different devices, modalities, and cases; therefore, with private datasets, it is very hard to see the state of the technology. Challenges such as EAD2020 are very important to compare medical imaging algorithms on a common platform and dataset, and to see the applicability of the CAD systems in clinics. In addition, getting researchers from different domains to come together and work on the same problem enables the acceleration of advancements in the field. These challenges bring healthcare experts and engineers together, resulting in more realistic works. Medical experts can observe the results and applicability of the systems in clinics while engineering people can see the real-life cases and prepare the models according to them. 

I thank the organizers of this challenge for providing us a great environment. I also thank NIHR BRC Oxford for financially supporting my participation in the entire IEEE ISBI2020 conference and travel grant support. EAD2020 Workshop was very informative and it was very valuable to engage with the organizers and experts in this domain. I hope this challenge series continues in the following years.Gorkem Polat is with the Department of Health Informatics in Turkey (https://hi.metu.edu.tr/) and he has won the Endoscopic Artefact Detection Challenge 2020 (EAD2020), hosted by Dr. Sharib Ali. The EAD2020 will be held in conjunction with the IEEE International Symposium of Biomedical Imaging 2020 (ISBI 2020). As MedIAN likes to support early career researchers and extend their network, they sponsored a full travel grant for his participation in the ISBI2020. Gorkem Polat is a PhD student at Middle East Technical University (METU), Ankara, Turkey. Previously he obtained his M.Sc. and a B.Sc. in Electrical-Electronics Engineering from the same university. His research interests include deep learning, machine learning, computer vision, object detection and medical image processing. His Ph.D. thesis is concerned with the inflammatory bowel disease detection in colonoscopy images. His project is a joint effort of the Institute of Gastroenterology at Marmara University and Deep Learning and Computer Vision Lab at METU.