DynBC evaluates candidate model updates by their agreement with the current model on augmented reference patches, filtering out updates that deviate too much, and reports improved robustness to client drift and catastrophic forgetting in histopathology segmentation.
FrOoDo: Framework for Out-of-Distribution Detection
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abstract
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.
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Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity
DynBC evaluates candidate model updates by their agreement with the current model on augmented reference patches, filtering out updates that deviate too much, and reports improved robustness to client drift and catastrophic forgetting in histopathology segmentation.