1 citations · 2 across the 9 of their papers we have counts for
9 papers
Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization
Isabella Poles, Simon Arberet, Riqiang Gao +5
Volumetric Modulated Arc Therapy (VMAT) is a cornerstone of modern radiation therapy, enabling highly conformal tumor irradiation and healthy-tissue sparing. Yet, its planning solv…
Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study
Yuhan Wang, Zihan Li, Han Liu +7
Voxel-wise dose prediction is a critical yet challenging task in practical radiotherapy (RT) planning, as bespoke models trained from scratch often struggle to generalize across di…
AI End-to-End Radiation Treatment Planning Under One Second
Simon Arberet, Riqiang Gao, Martin Kraus +9
Artificial intelligence-based radiation therapy (RT) planning has the potential to reduce planning time and inter-planner variability, improving efficiency and consistency in clini…
Demo: Generative AI helps Radiotherapy Planning with User Preference
Riqiang Gao, Simon Arberet, Martin Kraus +6
Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing deep learning approaches for 3D dose pr…
Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification
Han Liu, Bogdan Georgescu, Yanbo Zhang +8
3D medical image classification is essential for modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for scaling to new tasks, yet curre…
PanDx: AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT
Han Liu, Riqiang Gao, Eileen Krieg +1
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive forms of pancreatic cancer and is often diagnosed at an advanced stage due to subtle early imaging signs. To e…
A Beam's Eye View to Fluence Maps 3D Network for Ultra Fast VMAT Radiotherapy Planning
Simon Arberet, Florin C. Ghesu, Riqiang Gao +4
Volumetric Modulated Arc Therapy (VMAT) revolutionizes cancer treatment by precisely delivering radiation while sparing healthy tissues. Fluence maps generation, crucial in VMAT pl…
Automating RT Planning at Scale: High Quality Data For AI Training
Riqiang Gao, Mamadou Diallo, Han Liu +10
Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances with artificial intelligence (AI) promise to improve its precision and efficiency, but progress is o…
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
Riqiang Gao, Florin C. Ghesu, Simon Arberet +5
In contemporary radiotherapy planning (RTP), a key module leaf sequencing is predominantly addressed by optimization-based approaches. In this paper, we propose a novel deep reinfo…