of research. It goes beyond simply achieving reproducible results; it involves understanding the risks, improving research practices, and building trust in scientific outcomes. In some ways, it goes hand in hand with explainability. If works are explainable and transparent, they tend also to be reproducible. However, this is not always the case, and reproducibility is an umbrella term with many aspects behind it. 21 DAILY MICCAI Monday Virtual Imaging Platform (VIP) Differences in tumor segmentation outputs obtained with two different versions of Brain Tumor Segmentation (BraTS) pipeline on the same input image, as presented in [desligneris2023]. “This tutorial tackles one small aspect of reproducibility: computational reproducibility,” Sorina reveals. “You can have explainability and still don’t get computational reproducibility because lots of complex things happen at the computational level, and many younger researchers aren’t aware of that. If you have explainability, you’ll say, I obtained these results using this piece of software with this input data. You have the software, all the parameters, and all the data you need, but is that enough to have bitwise reproducibility? Are you able to obtain exactly the same results at
RkJQdWJsaXNoZXIy NTc3NzU=