ArticleJournal of digital imaging2021
A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 83 citations in OpenAlex.
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- Tongue Image-Based Diagnosis of Acute Respiratory Tract Infection Using Machine Learning: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective.Journal of imaging · 2025Review
- Dual-Stream Attention-Based Classification Network for Tibial Plateau Fractures via Diffusion Model Augmentation and Segmentation Map Integration.Current medical science · 2025Article
- Predicting alcohol use disorder risk in firefighters using a multimodal deep learning model: a cross-sectional study.Frontiers in psychiatry · 2025Article
- Development of a Transfer Learning-Based, Multimodal Neural Network for Identifying Malignant Dermatological Lesions From Smartphone Images.Cancer informatics · 2025Article
- A Novel Automatic Lung Nodule Classification Scheme using Fusion Ghost Convolution and Hybrid Normalization in Chest CTsCurrent medical imaging · 2025Article
- Integration of magnetic resonance imaging and deep learning for prostate cancer detection: a systematic review.American journal of clinical and experimental urology · 2025Review
- Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology.Diagnostics (Basel, Switzerland) · 2024Article
- Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration.Scientific reports · 2024Article
- An indigenous dataset for the detection and classification of apple leaf diseases.Data in brief · 2024Article
- Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review.Diagnostics (Basel, Switzerland) · 2024Review
- Reliability of Systematic and Targeted Biopsies versus Prostatectomy.Bioengineering (Basel, Switzerland) · 2023Article
- CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence.Healthcare (Basel, Switzerland) · 2023Article
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images.BMC bioinformatics · 2023Article
- Synthesizing realistic high-resolution retina image by style-based generative adversarial network and its utilization.Scientific reports · 2022Article
- A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their combinations have been investigated for various computer vision tasks in the context of deep learning, a specific work in the domain of medical imaging is rare and to the best of our knowledge, there has been no dedicated work on exploring the effects of various augmentation methods on the performance of deep learning models in prostate cancer detection. In this work, we have statically applied five most frequently used augmentation techniques (random rotation, horizontal flip, vertical flip, random crop, and translation) to prostate diffusion-weighted magnetic resonance imaging training dataset of 217 patients separately and evaluated the effect of each method on the accuracy of prostate cancer detection. The augmentation algorithms were applied independently to each data channel and a shallow as well as a deep convolutional neural network (CNN) was trained on the five augmented sets separately. We used area under receiver operating characteristic (ROC) curve (AUC) to evaluate the performance of the trained CNNs on a separate test set of 95 patients, using a validation set of 102 patients for finetuning. The shallow network outperformed the deep network with the best 2D slice-based AUC of 0.85 obtained by the rotation method.
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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.