ArticleProceedings of SPIE--the International Society for Optical Engineering2021
Random Search as a Neural Network Optimization Strategy for Convolutional-Neural-Network (CNN)-based Noise Reduction in CT.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Cerebro Wave Bee transformer to leverage EEG based hand movement classification using optimized bio-inspired learning rates.Scientific reports · 2026Article
- Ensemble localized patch residual convolution neural networks for pneumonia detection using chest X-ray.BMC medical informatics and decision making · 2026Article
- Machine learning models to predict 6-month mortality risk in home-based hospice patients with advanced cancer.Asia-Pacific journal of oncology nursing · 2025Article
- Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.Medical physics · 2024Article
- Developing a Machine-Learning Predictive Model for Retention of Posterior Cruciate Ligament in Patients Undergoing Total Knee Arthroplasty.Orthopaedic surgery · 2024Article
- Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices.Journal of personalized medicine · 2024Article
- Pie-Net: Prior-information-enabled deep learning noise reduction for coronary CT angiography acquired with a photon counting detector CT.Medical physics · 2023Article
- Contrast- and noise-dependent spatial resolution measurement for deep convolutional neural network-based noise reduction in CT using patient data.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Technical note: Phantom-based training framework for convolutional neural network CT noise reduction.Medical physics · 2023Article
- Dedicated convolutional neural network for noise reduction in ultra-high-resolution photon-counting detector computed tomography.Physics in medicine and biology · 2022Article
- A novel normalization algorithm to facilitate pre-assessment of Covid-19 disease by improving accuracy of CNN and its FPGA implementation.Evolving systems · 2022Article
- Dual-Contrast Biphasic Liver Imaging With Iodine and Gadolinium Using Photon-Counting Detector Computed Tomography: An Exploratory Animal Study.Investigative radiology · 2022Article
- Clinical evaluation of a phantom-based deep convolutional neural network for whole-body-low-dose and ultra-low-dose CT skeletal surveys.Skeletal radiology · 2022Article
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7 authors.
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Abstract
In this study, we describe a systematic approach to optimize deep-learning-based image processing algorithms using random search. The optimization technique is demonstrated on a phantom-based noise reduction training framework; however, the techniques described can be applied generally for other deep learning image processing applications. The parameter space explored included number of convolutional layers, number of filters, kernel size, loss function, and network architecture (either U-Net or ResNet). A total of 100 network models were examined (50 random search, 50 ablation experiments). Following the random search, ablation experiments resulted in a very minor performance improvement indicating near optimal settings were found during the random search. The top performing network architecture was a U-Net with 4 pooling layers, 64 filters, 3×3 kernel size, ELU activation, and a weighted feature reconstruction loss (0.2×VGG + 0.8×MSE). Relative to the low-dose input image, the CNN reduced noise by 90%, reduced RMSE by 34%, and increased SSIM by 76% on six patient exams reserved for testing. The visualization of hepatic and bone lesions was greatly improved following noise reduction.
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