English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Radiation therapy treatment planning is a complex process, as the target doseprescription and normal tissue sparing are conflicting objectives. In order toreduce human planning time efforts and improve the quality of treatmentplanning, knowledge-based planning (KBP) is in high demand. In this study, wepropose a novel learning-based ensemble approach, named LENAS, which integratesneural architecture search (NAS) with knowledge distillation for 3Dradiotherapy dose prediction. Specifically, the prediction network firstexhaustively searches each block from an enormous architecture space. Then,multiple architectures with promising performance and a large diversity areselected. To reduce the inference time, we adopt the teacher-student paradigmby treating the combination of diverse outputs from multiple learned networksas supervisions to guide the student network training. In addition, we applyadversarial learning to optimize the student network to recover the knowledgein teacher networks. To the best of our knowledge, this is the first attempt toinvestigate NAS and knowledge distillation in ensemble learning, especially inthe field of medical image analysis. The proposed method has been evaluated ontwo public datasets, i.e., the OpenKBP and AIMIS dataset. Extensiveexperimental results demonstrate the effectiveness of our method and itssuperior performance to the state-of-the-art methods. In addition, severalin-depth analysis and empirical guidelines are derived for ensemble learning.

LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction