FrOoDo: Framework for Out-of-Distribution Detection
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 3 of its 5 references that are in the map. First posted on arXiv 1 Aug 2022.
Builds on
3 of 5 references.
References · 3
- 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
- 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
- 2020Energy-based Out-of-distribution Detectioncited by 1,299
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2023Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learningcited by 5
- 2025Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuitycited by 2
- 2025ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learningcited by 1
- 2026MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradationscited by 0
- 2026MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradations8 Mar 2026
- 2025ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning4 Aug 2025
- 2025Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity8 Jan 2025
- 2023Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning1 Sep 2023