Gazegnn: A gaze-guided graph neural network for chest x-ray classification
Eye tracking research is important in computer vision because it can help us understand
how humans interact with the visual world. Specifically for high-risk applications, such as in …
how humans interact with the visual world. Specifically for high-risk applications, such as in …
Beam-wise dose composition learning for head and neck cancer dose prediction in radiotherapy
Automatic and accurate dose distribution prediction plays an important role in radiotherapy
plan. Although previous methods can provide promising performance, most methods did not …
plan. Although previous methods can provide promising performance, most methods did not …
Flexible-cm gan: Towards precise 3d dose prediction in radiotherapy
Deep learning has been utilized in knowledge-based radiotherapy planning in which a
system trained with a set of clinically approved plans is employed to infer a three …
system trained with a set of clinically approved plans is employed to infer a three …
DoseDiff: distance-aware diffusion model for dose prediction in radiotherapy
Treatment planning, which is a critical component of the radiotherapy workflow, is typically
carried out by a medical physicist in a time-consuming trial-and-error manner. Previous …
carried out by a medical physicist in a time-consuming trial-and-error manner. Previous …
SP-DiffDose: a conditional diffusion model for radiation dose prediction based on multi-scale fusion of anatomical structures, guided by SwinTransformer and projector
Radiation therapy serves as an effective and standard method for cancer treatment.
Excellent radiation therapy plans always rely on high-quality dose distribution maps …
Excellent radiation therapy plans always rely on high-quality dose distribution maps …
3D dose prediction for Gamma Knife radiosurgery using deep learning and data modification
Purpose To develop a machine learning-based, 3D dose prediction methodology for
Gamma Knife (GK) radiosurgery. The methodology accounts for cases involving targets of …
Gamma Knife (GK) radiosurgery. The methodology accounts for cases involving targets of …
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy
In contemporary radiotherapy planning (RTP), a key module leaf sequencing is
predominantly addressed by optimization-based approaches. In this paper, we propose a …
predominantly addressed by optimization-based approaches. In this paper, we propose a …
Single patient learning for adaptive radiotherapy dose prediction
Background Throughout a patient's course of radiation therapy, maintaining accuracy of their
initial treatment plan over time is challenging due to anatomical changes‐for example …
initial treatment plan over time is challenging due to anatomical changes‐for example …
Dose Prediction Driven Radiotherapy Paramters Regression via Intra-and Inter-Relation Modeling
J Cui, Y Xu, J Xiao, Y Fei, J Zhou, X Peng… - arXiv preprint arXiv …, 2024 - arxiv.org
Deep learning has facilitated the automation of radiotherapy by predicting accurate dose
distribution maps. However, existing methods fail to derive the desirable radiotherapy …
distribution maps. However, existing methods fail to derive the desirable radiotherapy …
FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose Prediction in Radiotherapy
Accurate dose distribution prediction is crucial in the radiotherapy planning. Although
previous methods based on convolutional neural network have shown promising …
previous methods based on convolutional neural network have shown promising …