29 DAILY MICCAI there is a family behind – but you should not think like that. I know it sounds crude, but it is more like an object, and you try to improve the situation somehow. So, you encourage even the most sensitive of young scholars now not to be afraid of getting into the medical field? There are still so many problems that need to be solved. Yeah! [she laughs] Your word for the community. Especially at MICCAI, I think we have the responsibility to bring everything that is developed in AI and computer vision closer to the patient. I don’t see this done in many research groups. Often, evaluation is not done in a rigorous way. People do just simple dataset splits, then evaluate the algorithm on this dataset split and say, okay, it has an accuracy of X and Y, and it works in this setting, but often in the real world, it does not really work. That is still a major problem. When I am at MICCAI, I see really exciting work, and you go through the posters, and often you see that validation was not conducted in the right way or that the test set is so limited that actually, you cannot really say something about the applicability of it. I think that it is still sad that we are at this point because we want to have an impact with our work, right? We really must work towards increasing the performance for clinicians. Monday Sandy Engelhardt Read 100 FASCINATING interviews with Women in Science!
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