Titlebook: Artificial Intelligence in Radiation Therapy; First International Dan Nguyen,Lei Xing,Steve Jiang Conference proceedings 2019 Springer Nat

originality

Individualized 3D Dose Distribution Prediction Using Deep Learning,tion. Qualitative measurements have showed analogous dose distributions and DVH curves compared to the true dose distribution. Quantitative measurements have demonstrated that our model can precisely predict the dose distribution with various trade-offs for different patients, with the largest mean

冒煙

Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy,thod includes a multi-task learning framework that combines a convolutional feature extraction and an embedded regression and classification based shape modeling. This enables the network to predict the deformable shape of an organ. We show that generative neural network-based shape modeling trained

LUCY

obnoxious

CBCT-Based Synthetic MRI Generation for CBCT-Guided Adaptive Radiotherapy, CBCT to MRI, which constrains the model by forcing a one-to-one mapping. A fully convolution neural network (FCN) with U-Net architecture is used in the generator to enable end-to-end CBCT-to-MRI transformations. Dense blocks and self-attention strategy are used to learn the information to well rep

Suppository

https://doi.org/10.1057/978-1-137-46178-0ss this, a reinforcement learning application of guided Monte Carlo tree search (GTS) was implemented, coupled with SL to guide the traversal through the tree, and update the fitness values of its nodes. To test the feasibility of GTS, 13 test prostate cancer patients were evaluated. Our results sho

synovial-joint

Orienting Frameworks and Concepts, symmetry in calculating image saliency of MRI images. The ratio of mean saliency value (RSal) from the propagated nodal volume on a weekly image to the mean saliency value of the pre-treatment nodal volume was calculated to assess whether the nodal volume shrank significantly. We evaluated our meth

內(nèi)向者

HEDGE

Orienting Frameworks and Concepts,ed by using the deep learning model and the actual position of the prostate were compared quantitatively. Differences between the predicted target positions using DNN and their actual positions are (mean ± standard deviation) . mm, . mm, and 1.64 ± 0.28 mm in anterior-posterior, lateral, and oblique

縮短

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