FrOoDo: Framework for Out-of-Distribution Detection
This provides a tool for researchers in digital pathology to streamline OoD detection tasks, but it is incremental as it builds on existing methods without introducing new detection techniques.
They tackled the problem of automating Out-of-Distribution detection evaluation in digital pathology by developing FrOoDo, a flexible framework that works with PyTorch models and allows researchers to focus on designing new models or methods.
FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology. It can be used with PyTorch classification and segmentation models, and its modular design allows for easy extension. The goal is to automate the task of OoD Evaluation such that research can focus on the main goal of either designing new models, new methods or evaluating a new dataset. The code can be found at https://github.com/MECLabTUDA/FrOoDo.