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

Chandelier

https://doi.org/10.1007/978-981-13-1715-6absolute error of dose volume histogram (DVH) and voxel-based mean absolute error were used to evaluate the prediction accuracy, with [0.9%, 1.9%] at PGTV, [1.1%, 2.8%] at PTV, [2.8%, 4.4%] at Lung, [3.5%, 6.9%] at Heart, [4.2%, 5.6%] at Spinal Cord, and [1.7%, 4.8%] at Body. These encouraging resul

圆锥体

无能力

Rajalakshmi Sriram,Rituparna Sarkartwork (one-DCN) is used for the correlation modeling. This model can predict the DVH of multiple OARs based on the individual patient’s geometry without manual removal of radiation plans with outliers. The average prediction error of the measurement focusing on the left lung, right lung, heart, spin

纠缠,缠绕

Prachee Joeg,Sneha Joshi,Rajalakshmi Sriramired brain dataset. The resulting CT scans were generated with the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) scores of 60.83 HU, 17.21 dB, and 0.8, respectively. DualGAN with perceptual loss function term and coordinate convolutional layer

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Men as Fathers: An Indian Perspectivetion. 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

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淡紫色花

Small-Intestine

Fathers, Caregiving and Social Change 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

挫败

Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Rg suboptimal and inefficient solutions. Column generation (CG) has been shown to produce superior plans compared to those of human selected beams, especially in highly non-coplanar plans such as 4π Radiotherapy. In this work, we applied AI to explore the decision space of beam orientation selection.

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