# No New-Net

arXiv: https://arxiv.org/abs/1809.10483 · HTML: https://mlsanity.com/paper/1809.10483/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 30 of 61)

- [Knowledge distillation from multi-modal to mono-modal segmentation networks](https://mlsanity.com/paper/2106.09564/) (2021)
- [Convolution-Free Medical Image Segmentation using Transformers](https://mlsanity.com/paper/2102.13645/) (2021)
- [QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results](https://mlsanity.com/paper/2112.10074/) (2021)
- [MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation](https://mlsanity.com/paper/2309.03329/) (2023)
- [Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient](https://mlsanity.com/paper/2103.06205/) (2021)
- [A review of deep learning-based information fusion techniques for multimodal medical image classification](https://mlsanity.com/paper/2404.15022/) (2024)
- [Cross-Modality Deep Feature Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2201.02356/) (2022)
- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge](https://mlsanity.com/paper/2405.18435/) (2024)
- [Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation](https://mlsanity.com/paper/2202.12295/) (2022)
- [Belief function-based semi-supervised learning for brain tumor segmentation](https://mlsanity.com/paper/2102.00097/) (2021)
- [On the relationship between calibrated predictors and unbiased volume estimation](https://mlsanity.com/paper/2112.12560/) (2021)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation](https://mlsanity.com/paper/2210.17398/) (2022)
- [Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms](https://mlsanity.com/paper/2101.07715/) (2021)
- [Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty](https://mlsanity.com/paper/2211.04161/) (2022)
- [HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images](https://mlsanity.com/paper/2103.16617/) (2021)
- [A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation](https://mlsanity.com/paper/2104.11020/) (2021)
- [HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging](https://mlsanity.com/paper/2202.05268/) (2022)
- [The Dice loss in the context of missing or empty labels: Introducing Phi and ε](https://mlsanity.com/paper/2207.09521/) (2022)
- [Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture](https://mlsanity.com/paper/2101.01546/) (2021)
- [ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?](https://mlsanity.com/paper/2408.10966/) (2024)
- [BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation](https://mlsanity.com/paper/2102.08990/) (2021)
- [Acquisition-invariant brain MRI segmentation with informative uncertainties](https://mlsanity.com/paper/2111.04094/) (2021)
- [Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2103.05529/) (2021)
- [Invertible Residual Network with Regularization for Effective Medical Image Segmentation](https://mlsanity.com/paper/2103.09042/) (2021)
- [Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review](https://mlsanity.com/paper/2103.13588/) (2021)
- [CateNorm: Categorical Normalization for Robust Medical Image Segmentation](https://mlsanity.com/paper/2103.15858/) (2021)
- [Brain tumour segmentation using a triplanar ensemble of U-Nets](https://mlsanity.com/paper/2105.11356/) (2021)
- [Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation](https://mlsanity.com/paper/2108.00713/) (2021)

## Abstract (arXiv)

In this paper we demonstrate the effectiveness of a well trained U-Net in the context of the BraTS 2018 challenge. This endeavour is particularly interesting given that researchers are currently besting each other with architectural modifications that are intended to improve the segmentation performance. We instead focus on the training process arguing that a well trained U-Net is hard to beat. Our baseline U-Net, which has only minor modifications and is trained with a large patch size and a Dice loss function indeed achieved competitive Dice scores on the BraTS2018 validation data. By incorporating additional measures such as region based training, additional training data, a simple postprocessing technique and a combination of loss functions, we obtain Dice scores of 77.88, 87.81 and 80.62, and Hausdorff Distances (95th percentile) of 2.90, 6.03 and 5.08 for the enhancing tumor, whole tumor and tumor core, respectively on the test data. This setup achieved rank two in BraTS2018, with more than 60 teams participating in the challenge.