mlsanityData to 14 Oct 2026

DeepDoseNet: A Deep Learning model for 3D Dose Prediction in Radiation Therapy

arXiv 2111.00077 ↗First posted 29 Oct 2021physics.med-ph cs.AI cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

The DeepDoseNet 3D dose prediction model based on ResNet and Dilated DenseNet is proposed. The 340 head-and-neck datasets from the 2020 AAPM OpenKBP challenge were utilized, with 200 for training, 40 for validation, and 100 for testing. Structures include 56Gy, 63Gy, 70Gy PTVs, and brainstem, spinal cord, right parotid, left parotid, larynx, esophagus, and mandible OARs. Mean squared error (MSE) loss, mean absolute error (MAE) loss, and MAE plus dose-volume histogram (DVH) based loss functions were investigated. Each model's performance was compared using a 3D dose score, barSD, (mean absolute difference between ground truth and predicted 3D dose distributions) and a DVH score, barSDVH (mean absolute difference between ground truth and predicted dose-volume metrics).Furthermore, DVH metrics Mean[Gy] and D0.1cc [Gy] for OARs and D99%, D95%, D1% for PTVs were computed. DeepDoseNet with the MAE plus DVH-based loss function had the best dose score performance of the OpenKBP entries. MAE+DVH model had the lowest prediction error (P<0.0001, Wilcoxon test) on validation and test datasets (validation: barSD=2.3Gy, barSDVH=1.9Gy; test: barSD=2.0Gy, barSDVH=1.6Gy) followed by the MAE model (validation: barSD=3.6Gy, barSDVH=2.4Gy; test: barSD=3.5Gy, barSDVH=2.3Gy). The MSE model had the highest prediction error (validation: barSD=3.7Gy, barSDVH=3.2Gy; test: barSD=3.6Gy, barSDVH=3.0Gy). No significant difference was found among models in terms of Mean [Gy], but the MAE+DVH model significantly outperformed the MAE and MSE models in terms of D0.1cc[Gy], particularly for mandible and parotids on both validation (P<0.01) and test (P<0.0001) datasets. MAE+DVH outperformed (P<0.0001) in terms of D99%, D95%, D1% for targets. MAE+DVH reduced barSD by ~60% and barSDVH by ~70%.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 63 references that are in the map. First posted on arXiv 29 Oct 2021.

Builds on

18 of 63 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  6. 2016Densely Connected Convolutional Networkscited by 6,720
  7. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  8. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  9. 2018Dual Attention Network for Scene Segmentationcited by 1,003
  10. 2020DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolutioncited by 222
  11. 2017Understanding Convolution for Semantic Segmentationcited by 168
  12. 2018A Deep Learning Framework for Unsupervised Affine and Deformable Image Registrationcited by 160
6 more
  1. 2017Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNetscited by 32
  2. 2020OpenKBP: The open-access knowledge-based planning grand challengecited by 30
  3. 2018Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecturecited by 18
  4. 2018Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurationscited by 12
  5. 2019Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapycited by 10
  6. 20173D Densely Convolutional Networks for Volumetric Segmentationcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2023Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatmentscited by 2
  2. 2024Deep Evidential Learning for Radiotherapy Dose Predictioncited by 2
  3. 2026Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Studycited by 1