# Auto-nnU-Net: Towards Automated Medical Image Segmentation

arXiv: https://arxiv.org/abs/2505.16561 · HTML: https://mlsanity.com/paper/2505.16561/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 80 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://mlsanity.com/paper/2306.00989/) (2023)
- [Non-stochastic Best Arm Identification and Hyperparameter Optimization](https://mlsanity.com/paper/1502.07943/) (2015)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization](https://mlsanity.com/paper/2204.11051/) (2022)
- [C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/1912.09628/) (2019)
- [DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.15954/) (2021)
- [Segment Anything in Medical Images and Videos: Benchmark and Deployment](https://mlsanity.com/paper/2408.03322/) (2024)
- [Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search](https://mlsanity.com/paper/1807.06906/) (2018)
- [PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning](https://mlsanity.com/paper/2306.12370/) (2023)
- [V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1906.02817/) (2019)
- [Multi-objective Asynchronous Successive Halving](https://mlsanity.com/paper/2106.12639/) (2021)
- [Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization](https://mlsanity.com/paper/2105.01015/) (2021)
- [Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2006.05847/) (2020)
- [Zero-Shot AutoML with Pretrained Models](https://mlsanity.com/paper/2206.08476/) (2022)
- [T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging](https://mlsanity.com/paper/2111.07535/) (2021)
- [Learning Multiple Defaults for Machine Learning Algorithms](https://mlsanity.com/paper/1811.09409/) (2018)
- [Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars](https://mlsanity.com/paper/2211.01842/) (2022)

## Built on by (most cited first, 3 of 3)

- [Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation](https://mlsanity.com/paper/2510.23241/) (2025)
- [Insights from Autoresearch for Solar Panel Segmentation](https://mlsanity.com/paper/2610.10491/) (2026)
- [FALCON: Few-Shot Adversarial Learning for Cross-Domain Medical Image Segmentation](https://mlsanity.com/paper/2601.01687/) (2026)

## Abstract (arXiv)

Medical Image Segmentation (MIS) includes diverse tasks, from bone to organ segmentation, each with its own challenges in finding the best segmentation model. The state-of-the-art AutoML-related MIS-framework nnU-Net automates many aspects of model configuration but remains constrained by fixed hyperparameters and heuristic design choices. As a full-AutoML framework for MIS, we propose Auto-nnU-Net, a novel nnU-Net variant enabling hyperparameter optimization (HPO), neural architecture search (NAS), and hierarchical NAS (HNAS). Additionally, we propose Regularized PriorBand to balance model accuracy with the computational resources required for training, addressing the resource constraints often faced in real-world medical settings that limit the feasibility of extensive training procedures. We evaluate our approach across diverse MIS datasets from the well-established Medical Segmentation Decathlon, analyzing the impact of AutoML techniques on segmentation performance, computational efficiency, and model design choices. The results demonstrate that our AutoML approach substantially improves the segmentation performance of nnU-Net on 6 out of 10 datasets and is on par on the other datasets while maintaining practical resource requirements. Our code is available at https://github.com/automl/AutoNNUnet.