ArticleJournal of imaging2023
Data Augmentation in Classification and Segmentation: A Survey and New Strategies.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
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- A High-Resolution Multifocal RGB Pollen Grain Image Dataset for Deep Learning Computer Vision Tasks from Biobío Region, Chile.Scientific data · 2026Article
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- Compact deep learning models for colon histopathology focusing performance and generalization challenges.Scientific reports · 2026Article
- Transfer learning with Bayesian optimization for colorectal cancer histopathology classification.BMC medical imaging · 2026Article
- EEG Signal Classification with Data Augmentation for Epileptic Focus Localization and Deep Sleep Detection.Sensors (Basel, Switzerland) · 2026Article
- A Hierarchical Deep Learning Architecture for Diagnosing Retinal Diseases Using Cross-Modal OCT to Fundus Translation in the Lack of Paired Data.Journal of imaging · 2026Article
- Deep Learning-Based Segmentation of the Ulnar Nerve in Ultrasound Images.Medicina (Kaunas, Lithuania) · 2026Article
- InnerEye-HS: a disease-agnostic clinical tool for hippocampal segmentation.Brain communications · 2026Article
- Anaemia: A Scientific Overview on Types and Recent Advancements in the Detection of Anaemia.Cardiovascular & hematological agents in medicinal chemistry · 2026Review
- Explainable deep learning ensemble framework for accurate classification of wild poisonous mushroom species.BMC biotechnology · 2025Article
- Bridging classical and neural methods for improved segmentation in mathematical text based images.Scientific reports · 2025Article
- Deep-learning based model for sperm morphology assessment using the SMD/MSS dataset.Future science OA · 2025Article
- Explainable convolutional neural network architectures for high-performance taxonomic classification of gasteroid macrofungi.Scientific reports · 2025Article
- Deep learning for sports motion recognition with a high-precision framework for performance enhancement.Scientific reports · 2025Article
- Effectiveness of traditional augmentation methods for rebar counting using UAV imagery with Faster R-CNN and YOLOv10-based transformer architectures.Scientific reports · 2025Article
- Explainable Transformer-Based Framework for Glaucoma Detection from Fundus Images Using Multi-Backbone Segmentation and vCDR-Based Classification.Diagnostics (Basel, Switzerland) · 2025Article
- A Novel Hybrid Technique for Detecting and Classifying Hyperspectral Images of Tomato Fungal Diseases Based on Deep Feature Extraction and Manhattan Distance.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the past decade, deep neural networks, particularly convolutional neural networks, have revolutionised computer vision. However, all deep learning models may require a large amount of data so as to achieve satisfying results. Unfortunately, the availability of sufficient amounts of data for real-world problems is not always possible, and it is well recognised that a paucity of data easily results in overfitting. This issue may be addressed through several approaches, one of which is data augmentation. In this paper, we survey the existing data augmentation techniques in computer vision tasks, including segmentation and classification, and suggest new strategies. In particular, we introduce a way of implementing data augmentation by using local information in images. We propose a parameter-free and easy to implement strategy, the random local rotation strategy, which involves randomly selecting the location and size of circular regions in the image and rotating them with random angles. It can be used as an alternative to the traditional rotation strategy, which generally suffers from irregular image boundaries. It can also complement other techniques in data augmentation. Extensive experimental results and comparisons demonstrated that the new strategy consistently outperformed its traditional counterparts in, for example, image classification.
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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.