# LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction arXiv: https://arxiv.org/abs/2106.06733 ยท HTML: https://mlsanity.com/paper/2106.06733/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. - Builds on 30 of its 86 references that are in the map. - First posted on arXiv 12 Jun 2021. ## Placed in - Knowledge distillation / [Distillation for medical imaging](https://mlsanity.com/distill/distillation-for-medical-imaging/) ## Builds on (most cited first, 30 of 30) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014) - [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016) - [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016) - [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018) - [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019) - [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017) - [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018) - [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017) - [Insights on representational similarity in neural networks with canonical correlation](https://mlsanity.com/paper/1806.05759/) (2018) - [Feature Pyramid Transformer](https://mlsanity.com/paper/2007.09451/) (2020) - [Neural Ensemble Search for Uncertainty Estimation and Dataset Shift](https://mlsanity.com/paper/2006.08573/) (2020) - [A Survey on Ensemble Learning under the Era of Deep Learning](https://mlsanity.com/paper/2101.08387/) (2021) - [Angle-based Search Space Shrinking for Neural Architecture Search](https://mlsanity.com/paper/2004.13431/) (2020) - [OpenKBP: The open-access knowledge-based planning grand challenge](https://mlsanity.com/paper/2011.14076/) (2020) - [V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1906.02817/) (2019) - [When does Diversity Help Generalization in Classification Ensembles?](https://mlsanity.com/paper/1910.13631/) (2019) - [Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture](https://mlsanity.com/paper/1805.10397/) (2018) - [Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations](https://mlsanity.com/paper/1812.06934/) (2018) - [One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking](https://mlsanity.com/paper/2104.00597/) (2021) - [Incorporating 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 therapy](https://mlsanity.com/paper/1908.05874/) (2019) - [Sub-Architecture Ensemble Pruning in Neural Architecture Search](https://mlsanity.com/paper/1910.00370/) (2019) - [Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2002.09625/) (2020) - [Dose Prediction with Deep Learning for Prostate Cancer Radiation Therapy: Model Adaptation to Different Treatment Planning Practices](https://mlsanity.com/paper/2006.16481/) (2020) - [ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection](https://mlsanity.com/paper/2003.08770/) (2020) - [Using Deep Learning to Predict Beam-Tunable Pareto Optimal Dose Distribution for Intensity Modulated Radiation Therapy](https://mlsanity.com/paper/2006.11236/) (2020) ## Built on by (most cited first, 3 of 3) - [Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection](https://mlsanity.com/paper/2409.17485/) (2024) - [Rethinking Boundary Detection in Deep Learning-Based Medical Image Segmentation](https://mlsanity.com/paper/2505.04652/) (2025) - [Clinical DVH metrics as a loss function for 3D dose prediction in head and neck radiotherapy](https://mlsanity.com/paper/2603.29670/) (2026) ## Abstract (arXiv) Radiation therapy treatment planning requires balancing the delivery of the target dose while sparing normal tissues, making it a complex process. To streamline the planning process and enhance its quality, there is a growing demand for knowledge-based planning (KBP). Ensemble learning has shown impressive power in various deep learning tasks, and it has great potential to improve the performance of KBP. However, the effectiveness of ensemble learning heavily depends on the diversity and individual accuracy of the base learners. Moreover, the complexity of model ensembles is a major concern, as it requires maintaining multiple models during inference, leading to increased computational cost and storage overhead. In this study, we propose a novel learning-based ensemble approach named LENAS, which integrates neural architecture search with knowledge distillation for 3D radiotherapy dose prediction. Our approach starts by exhaustively searching each block from an enormous architecture space to identify multiple architectures that exhibit promising performance and significant diversity. To mitigate the complexity introduced by the model ensemble, we adopt the teacher-student paradigm, leveraging the diverse outputs from multiple learned networks as supervisory signals to guide the training of the student network. Furthermore, to preserve high-level semantic information, we design a hybrid-loss to optimize the student network, enabling it to recover the knowledge embedded within the teacher networks. The proposed method has been evaluated on two public datasets, OpenKBP and AIMIS. Extensive experimental results demonstrate the effectiveness of our method and its superior performance to the state-of-the-art methods.