mlsanityData to 7 Oct 2026

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

arXiv 1810.07884 ↗First posted 18 Oct 2018cs.CVcited by 36

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Abstract (arXiv)

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2018.

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Citing papers per year: 2021: 4; 2022: 5; 2023: 14; 2024: 9; 2025: 3; 2026: 1.

36 papers.

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  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  2. 2024Understanding Test-Time Augmentationcited by 23
  3. 2024Tyche: Stochastic In-Context Learning for Medical Image Segmentationcited by 20
  4. 2022Learning from partially labeled data for multi-organ and tumor segmentationcited by 18
  5. 2021Uncertainty-Guided Progressive GANs for Medical Image Translationcited by 16
  6. 2023Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learningcited by 16
  7. 2024Uncertainty-aware Medical Diagnostic Phrase Identification and Groundingcited by 13
  8. 2023SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledgecited by 10
  9. 2023Robust Uncertainty Quantification Using Conformalised Monte Carlo Predictioncited by 10
  10. 2023Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Imagescited by 7
  11. 2024Test-time generative augmentation for medical image segmentationcited by 6
  12. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
24 more
  1. 2024BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averagingcited by 5
  2. 2022Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projectioncited by 3
  3. 2023Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertaintycited by 3
  4. 2023Uncertainty-Aware AB3DMOT by Variational 3D Object Detectioncited by 3
  5. 2024Test-Time Augmentation Meets Variational Bayescited by 3
  6. 2025Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropoutcited by 3
  7. 2021Adaptive Test-Time Augmentation for Low-Power CPUcited by 2
  8. 2023Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applicationscited by 2
  9. 2024TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Visioncited by 2
  10. 2021A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Predictioncited by 1
  11. 2023Uncertainty Quantification for Image-based Traffic Prediction across Citiescited by 1
  12. 2023Sense, Predict, Adapt, Repeat: A Blueprint for Design of New Adaptive AI-Centric Sensing Systemscited by 1
  13. 2025How To Make Your Cell Tracker Say "I dunno!"cited by 1
  14. 2022SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image Analysiscited by 0
  15. 2022Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demandcited by 0
  16. 2022Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Modelscited by 0
  17. 2023Variational Voxel Pseudo Image Trackingcited by 0
  18. 2023A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentationcited by 0
  19. 2023Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentationcited by 0
  20. 2023A hybrid approach for improving U-Net variants in medical image segmentationcited by 0
  21. 2023HAct: Out-of-Distribution Detection with Neural Net Activation Histogramscited by 0
  22. 2024Dual-Channel Reliable Breast Ultrasound Image Classification Based on Explainable Attribution and Uncertainty Quantificationcited by 0
  23. 2025Variational Graph Convolutional Neural Networkscited by 0
  24. 2026Quantification of Uncertainty with Adversarial Models in Medical Image Segmentationcited by 0