mlsanityData to 7 Oct 2026

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

arXiv 1711.01468 ↗First posted 4 Nov 2017cs.CV cs.AI cs.LGcited by 58

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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.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Nov 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 9; 2023: 10; 2024: 11; 2025: 3; 2026: 2.

58 papers.

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  1. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  2. 2021Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditionscited by 68
  3. 2021Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalitiescited by 42
  4. 2021Extending nn-UNet for brain tumor segmentationcited by 36
  5. 2024How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentationcited by 27
  6. 2024A review of deep learning-based information fusion techniques for multimodal medical image classificationcited by 25
  7. 2022Cross-Modality Deep Feature Learning for Brain Tumor Segmentationcited by 22
  8. 2021ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scanscited by 18
  9. 2024Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directionscited by 17
  10. 2021The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sectorcited by 16
  11. 2021Orthogonal Ensemble Networks for Biomedical Image Segmentationcited by 16
  12. 2021Medical Image Segmentation with 3D Convolutional Neural Networks: A Surveycited by 16
38 more
  1. 2022Evidence fusion with contextual discounting for multi-modality medical image segmentationcited by 12
  2. 2022Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentationcited by 11
  3. 2021A Tri-attention Fusion Guided Multi-modal Segmentation Networkcited by 10
  4. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detectioncited by 10
  5. 2023Effect of latent space distribution on the segmentation of images with multiple annotationscited by 7
  6. 2022Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-Viewcited by 6
  7. 2023H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentationcited by 6
  8. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  9. 2024Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignmentscited by 6
  10. 2021Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Predictioncited by 5
  11. 2022Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directionscited by 5
  12. 2021Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learningcited by 4
  13. 2021SUPER-Net: Trustworthy Image Segmentation via Uncertainty Propagation in Encoder-Decoder Networkscited by 4
  14. 2021Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibrationcited by 4
  15. 2022Application of belief functions to medical image segmentation: A reviewcited by 4
  16. 20213D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraintcited by 3
  17. 2021BEDS: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentationcited by 3
  18. 2023Multiscale Progressive Text Prompt Network for Medical Image Segmentationcited by 3
  19. 2024DeepCERES: A Deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRIcited by 3
  20. 2024Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networkscited by 3
  21. 2021Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentationcited by 2
  22. 2021Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Reviewcited by 2
  23. 2021Brain tumour segmentation using a triplanar ensemble of U-Netscited by 2
  24. 2021Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRIcited by 2
  25. 2022A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtractioncited by 2
  26. 2024Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectivescited by 2
  27. 2021Multi-stage Deep Layer Aggregation for Brain Tumor Segmentationcited by 1
  28. 2021A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Predictioncited by 1
  29. 2021Automated Segmentation and Volume Measurement of Intracranial Carotid Artery Calcification on Non-Contrast CTcited by 1
  30. 2022Fitting Segmentation Networks on Varying Image Resolutions using Splattingcited by 1
  31. 20233D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentationcited by 1
  32. 2024Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor Segmentationcited by 1
  33. 2025Multi-encoder nnU-Net outperforms transformer models with self-supervised pretrainingcited by 1
  34. 2021Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastomacited by 0
  35. 2021Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Futurecited by 0
  36. 2023Medical Image Analysis using Deep Relational Learningcited by 0
  37. 2023Ensemble Methods for Multi-Organ Segmentation in CT Seriescited by 0
  38. 2023A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentationcited by 0

50 most cited of 58 All 58 (JSON)