# Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

arXiv: https://arxiv.org/abs/1711.01468 · HTML: https://mlsanity.com/paper/1711.01468/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2017.

## Built on by (most cited first, 30 of 58)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions](https://mlsanity.com/paper/2104.03829/) (2021)
- [Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities](https://mlsanity.com/paper/2104.06231/) (2021)
- [Extending nn-UNet for brain tumor segmentation](https://mlsanity.com/paper/2112.04653/) (2021)
- [How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation](https://mlsanity.com/paper/2402.17317/) (2024)
- [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)
- [ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans](https://mlsanity.com/paper/2103.02561/) (2021)
- [Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2412.02621/) (2024)
- [The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector](https://mlsanity.com/paper/2104.07468/) (2021)
- [Orthogonal Ensemble Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.10827/) (2021)
- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [Evidence fusion with contextual discounting for multi-modality medical image segmentation](https://mlsanity.com/paper/2206.11739/) (2022)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [A Tri-attention Fusion Guided Multi-modal Segmentation Network](https://mlsanity.com/paper/2111.01623/) (2021)
- [MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection](https://mlsanity.com/paper/2502.16943/) (2025)
- [Effect of latent space distribution on the segmentation of images with multiple annotations](https://mlsanity.com/paper/2304.13476/) (2023)
- [Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View](https://mlsanity.com/paper/2206.14746/) (2022)
- [H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation](https://mlsanity.com/paper/2307.01486/) (2023)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments](https://mlsanity.com/paper/2412.13533/) (2024)
- [Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction](https://mlsanity.com/paper/2104.01149/) (2021)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning](https://mlsanity.com/paper/2107.00719/) (2021)
- [SUPER-Net: Trustworthy Image Segmentation via Uncertainty Propagation in Encoder-Decoder Networks](https://mlsanity.com/paper/2111.05978/) (2021)
- [Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration](https://mlsanity.com/paper/2111.07716/) (2021)
- [Application of belief functions to medical image segmentation: A review](https://mlsanity.com/paper/2205.01733/) (2022)
- [3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint](https://mlsanity.com/paper/2102.03111/) (2021)
- [BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation](https://mlsanity.com/paper/2102.08990/) (2021)
- [Multiscale Progressive Text Prompt Network for Medical Image Segmentation](https://mlsanity.com/paper/2307.00174/) (2023)

## Abstract (arXiv)

Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the various proposed networks perform differently, with behaviour largely influenced by architectural choices and training settings. This paper explores Ensembles of Multiple Models and Architectures (EMMA) for robust performance through aggregation of predictions from a wide range of methods. The approach reduces the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database. EMMA can be seen as an unbiased, generic deep learning model which is shown to yield excellent performance, winning the first position in the BRATS 2017 competition among 50+ participating teams.