ArticleScientific reports2020
DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Gamma Knife treatment planning using knowledge-based reinforcement learning.Medical physics · 2026Article
- Feasibility study of three-dimensional dose prediction of esophageal cancer radiotherapy based on CGAN.Journal of applied clinical medical physics · 2026Article
- Evaluating dose distribution in prostate IMRT patients using deep learning: the influence of loss function on model performance.Physical and engineering sciences in medicine · 2026Article
- Design and validation of a deep learning GAN for predictingJournal of applied clinical medical physics · 2026Article
- Review
- Deep-Learning-Based 3D Dose Distribution Prediction for VMAT Lung Cancer Treatment Using an Enhanced UNet3D Architecture with Composite Loss Functions.Bioengineering (Basel, Switzerland) · 2026Article
- DeepSMCP - Deep-learning powered denoising of Monte Carlo dose distributions within the Swiss Monte Carlo Plan.Zeitschrift fur medizinische Physik · 2026Article
- SegMambaDP: a long-range sequential modeling mamba network for 3D dose prediction in radiotherapy.BMC medical imaging · 2025Article
- A few-shot u-net learning framework for fast and accurate three-dimensional dose prediction in radiotherapy.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2025Article
- NRG Oncology Assessment of Artificial Intelligence for Automatic Treatment Planning in Radiation Therapy Clinical Trials: Present and Future.International journal of radiation oncology, biology, physics · 2025Article
- Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma Radiotherapy.Journal of imaging informatics in medicine · 2025Article
- Deep Learning-Based Heterogeneity Correction of the Homogeneous Dose Distribution for Single Brain Tumors in Gamma Knife Radiosurgery.Advances in radiation oncology · 2025Article
- Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT.Precision radiation oncology · 2025Article
- Deep learning architecture with shunted transformer and 3D deformable convolution for voxel-level dose prediction of head and neck tumors.Physical and engineering sciences in medicine · 2024Article
- Enhanced 3D dose prediction for hypofractionated SRS (gamma knife radiosurgery) in brain tumor using cascaded-deep-supervised convolutional neural network.Physical and engineering sciences in medicine · 2024Article
- Attention-Gated Deep-Learning-Based Automatic Digitization of Interstitial Needles in High-Dose-Rate Brachytherapy for Cervical Cancer.Advances in radiation oncology · 2024Article
- Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications.NPJ digital medicine · 2023Article
- Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data.Frontiers in oncology · 2023Article
- Evaluation of a hybrid automatic planning solution for rectal cancer.Radiation oncology (London, England) · 2022Article
- An automatic method to generate voxel-based absorbed doses from radioactivity distributions for nuclear medicine using generative adversarial networks: a feasibility study.Physical and engineering sciences in medicine · 2022Article
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8 authors.
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Abstract
Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Generative adversarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is better suited to learn from heterogeneous labels. However, GANs are difficult to train and rely on compromised architectures to facilitate convergence. This study suggests an attention-gated generative adversarial network (DoseGAN) to improve learning, increase model complexity, and reduce network redundancy by focusing on relevant anatomy. DoseGAN was compared to alternative state-of-the-art dose prediction algorithms using heterogeneity index, conformity index, and various dosimetric parameters. All algorithms were trained, validated, and tested using 141 prostate SBRT patients. DoseGAN was able to predict more realistic volumetric dosimetry compared to all other algorithms and achieved statistically significant improvement compared to all alternative algorithms for the V
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