Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatments

arXiv:2311.06572v2 Announce Type: replace-cross
Abstract: The field of Radiation Oncology is uniquely positioned to benefit from the use of artificial intelligence to fully automate the creation of radiation treatment plans for cancer therapy. This time-consuming and specialized task combines patient imaging with organ and tumor segmentation to generate a 3D radiation dose distribution to meet clinical treatment goals, similar to voxel-level dense prediction. In this work, we propose Swin UNETR++, that contains a lightweight 3D Dual Cross-Attention (DCA) module to …

abstract artificial artificial intelligence arxiv automate automated benefit cancer cs.cv eess.iv imaging intelligence oncology patient prediction segmentation swin therapy transformer treatment type