1 citations · 2 across the 9 of their papers we have counts for
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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…
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…