# FrOoDo: Framework for Out-of-Distribution Detection

arXiv: https://arxiv.org/abs/2208.00963 · HTML: https://mlsanity.com/paper/2208.00963/

## 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 (most cited first, 3 of 3)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)

## Built on by (most cited first, 4 of 4)

- [Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning](https://mlsanity.com/paper/2309.00688/) (2023)
- [Federated-Continual Dynamic Segmentation of Histopathology guided by Barlow Continuity](https://mlsanity.com/paper/2501.04588/) (2025)
- [ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning](https://mlsanity.com/paper/2508.02414/) (2025)
- [MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradations](https://mlsanity.com/paper/2603.07769/) (2026)

## 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.