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

空隙

書目名稱Artificial Intelligence in Radiation Therapy影響因子(影響力)




書目名稱Artificial Intelligence in Radiation Therapy影響因子(影響力)學科排名




書目名稱Artificial Intelligence in Radiation Therapy網(wǎng)絡公開度




書目名稱Artificial Intelligence in Radiation Therapy網(wǎng)絡公開度學科排名




書目名稱Artificial Intelligence in Radiation Therapy被引頻次




書目名稱Artificial Intelligence in Radiation Therapy被引頻次學科排名




書目名稱Artificial Intelligence in Radiation Therapy年度引用




書目名稱Artificial Intelligence in Radiation Therapy年度引用學科排名




書目名稱Artificial Intelligence in Radiation Therapy讀者反饋




書目名稱Artificial Intelligence in Radiation Therapy讀者反饋學科排名

蝕刻

Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiatio 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

hemophilia

恫嚇

大暴雨

A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN andod for . prostate lesion detection and classification, using input sequences of T2-weighted images, apparent diffusion coefficient (ADC) maps and high b-value diffusion-weighted images. In the first stage, a Mask R-CNN model is trained to automatically segment prostate structures. In the second stag

output

慟哭

Voxel-Level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions,absolute 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

有限

Online Target Volume Estimation and Prediction from an Interlaced Slice Acquisition - A Manifold Emures as targets. Locally linear embedding (LLE) was combined with manifold alignment to establish correspondence across slice positions. Multislice target contours were generated using a LLE-based motion model for each real-time image. Motion predictions were performed using a weighted k-nearest nei

Urologist

One-Dimensional Convolutional Network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Raditwork (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

arousal