ReviewJournal of imaging2022
Image Augmentation Techniques for Mammogram Analysis.
Review in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 105 citations in OpenAlex.
- Mammo-GAN-Assisted Deep Network Training Scheme for Lesion Detection.Journal of imaging informatics in medicine · 2026Article
- Article
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- CADxPolydetect: a clinically explainable hybrid deep learning system for multi-class colorectal lesion detection using augmented colonoscopy images.BMC medical informatics and decision making · 2025Article
- Enhancing Breast Lesion Detection in Mammograms via Transfer Learning.Journal of imaging · 2025Article
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- Neural Network-Based Mammography Analysis: Augmentation Techniques for Enhanced Cancer Diagnosis-A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Mam-Incept-Net: a novel inception model for precise interpretation of mammography images.PeerJ. Computer science · 2025Article
- Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.Scientific reports · 2024Article
- Optimizing mammography interpretation education: leveraging deep learning for cohort-specific error detection to enhance radiologist training.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis.Biomedical engineering letters · 2024Article
- DraiNet: AI-driven decision support in pneumothorax and pleural effusion management.Pediatric surgery international · 2023Article
- EnsemDeepCADx: Empowering Colorectal Cancer Diagnosis with Mixed-Dataset Features and Ensemble Fusion CNNs on Evidence-Based CKHK-22 Dataset.Bioengineering (Basel, Switzerland) · 2023Article
- Application of Deep Learning System Technology in Identification of Women's Breast Cancer.Medicina (Kaunas, Lithuania) · 2023Article
- Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition.Sensors (Basel, Switzerland) · 2023Article
- Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance.Scientific reports · 2023Article
- Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis.Bioengineering (Basel, Switzerland) · 2023Article
- ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images.Diagnostics (Basel, Switzerland) · 2022Article
- MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique.Frontiers in medicine · 2022Article
- High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection.Frontiers in oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods' performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.
Indexed as
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What OpenQuestion holds
Registered trials
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.