Artificial Intelligence in Radiation Therapy: First International Workshop, AIRT 2019, Held in Conju
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- Artikel-Nr.: 10395367
Beschreibung
Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy.- Feasibility of CT-only 3D dose prediction for VMAT prostate plans using deep learning.- Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency.- 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network.- Toward markerless image-guided radiotherapy using deep learning for prostate cancer.- A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network.- A Novel Deep Learning Framework for Standardizing the Label of OARs in CT.- Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery.- Voxel-level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions.- Online Target Volume Estimation and Prediction From an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach.- One-dimensional convolutional network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning.- Unpaired Synthetic Image Generation in Radiology Using GANs.- Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study.- Individualized 3D Dose Distribution Prediction Using Deep Learning.- Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy.- Dose Distribution Prediction for Optimal Treatment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma.- DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy.- UC-GAN for MR to CT Image Synthesis.- CBCT-based Synthetic MRI Generation for CBCT-guided Adaptive Radiotherapy.- Cardio-pulmonary Substructure Segmentation of CT images using Convolutional Neural Networks.
Eigenschaften
Breite: | 156 |
Gewicht: | 290 g |
Höhe: | 236 |
Länge: | 13 |
Seiten: | 172 |
Sprachen: | Englisch |
Autor: | Dan Nguyen, Lei Xing, Steve Jiang |
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