ReviewFrontiers in computational neuroscience2019
Data Augmentation for Brain-Tumor Segmentation: A Review.
Review in Frontiers in computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.
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Who cites it
73 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of radiomics to meningiomas: A systematic review.Neuro-oncology · 2023Pooled it
- Deep Learning-Assisted Three-Dimensional Segmentation of Vertebrobasilar Artery Calcification in Cone Beam Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Importance of the Quality of Annotation: Impact of Simulated Inter-Observer Variability on Deep Neural Network Automated Segmentation Model Performance.Bioengineering (Basel, Switzerland) · 2026Article
- A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system.Scientific reports · 2026Article
- Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.Nature aging · 2026Article
- BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection.Brain informatics · 2026Article
- Automated detection of fetal vascular malperfusion via data augmentation and algorithm improvement.Scientific reports · 2026Article
- A Review of U-Net Based Deep Learning Frameworks for MRI-Based Brain Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2026Review
- An efficient brain tumor classification system with accurate recognition and complementary downsampling blocks.PloS one · 2026Article
- Image Processing for Diagnosing Psoriasis: A Machine Learning Approach to Classify Skin Lesions into Psoriasis Subtypes.Medical journal of the Islamic Republic of Iran · 2026Article
- Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.Brain informatics · 2025Review
- Deep Learning Model for Automated Segmentation of Orbital Structures in MRI Images.Clinical neuroradiology · 2025Article
- The role of data augmentation and attention mechanisms in UNet and ConvNeXt architectures for optimizing breast tumor segmentation.Scientific reports · 2025Article
- Multi-Label Conditioned Diffusion for Cardiac MR Image Augmentation and Segmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Design of a deep fusion model for early Parkinson's disease prediction using handwritten image analysis.Scientific reports · 2025Article
- Article
- Augmenting atmospheric turbulence effects on thermal-adapted deep object detection models.Scientific reports · 2025Article
- An Enhanced Approach Using AGS Network for Skin Cancer Classification.Sensors (Basel, Switzerland) · 2025Article
- Ratio maps of T1w/T2w MRI signal intensity do not improve deep-learning segmentation of pediatric brain tumors.PloS one · 2025Article
- Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images.Scientific reports · 2024Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.