FluenceFormer: Transformer for Radiotherapy Planning
Analysis
This paper introduces FluenceFormer, a transformer-based framework for radiotherapy planning. It addresses the limitations of previous convolutional methods in capturing long-range dependencies in fluence map prediction, which is crucial for automated radiotherapy planning. The use of a two-stage design and the Fluence-Aware Regression (FAR) loss, incorporating physics-informed objectives, are key innovations. The evaluation across multiple transformer backbones and the demonstrated performance improvement over existing methods highlight the significance of this work.
Key Takeaways
- •Proposes FluenceFormer, a transformer-based framework for fluence map regression in radiotherapy planning.
- •Employs a two-stage design and the Fluence-Aware Regression (FAR) loss for improved performance.
- •Demonstrates superior performance compared to existing methods, particularly with Swin UNETR backbone.
- •Addresses the limitations of convolutional methods in capturing long-range dependencies.
“FluenceFormer with Swin UNETR achieves the strongest performance among the evaluated models and improves over existing benchmark CNN and single-stage methods, reducing Energy Error to 4.5% and yielding statistically significant gains in structural fidelity (p < 0.05).”