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ORCID1st AIRT@MICCAI 2019: Shenzhen, China
- Dan Nguyen, Lei Xing, Steve B. Jiang:
Artificial Intelligence in Radiation Therapy - First International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. Lecture Notes in Computer Science 11850, Springer 2019, ISBN 978-3-030-32485-8 - Azar Sadeghnejad-Barkousaraie
, Olalekan P. Ogunmolu, Steve B. Jiang, Dan Nguyen:
Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy. 1-9 - Siri Willems, Wouter Crijns, Edmond Sterpin, Karin Haustermans, Frederik Maes:
Feasibility of CT-Only 3D Dose Prediction for VMAT Prostate Plans Using Deep Learning. 10-17 - Yu-Chi Hu, Cynthia Polvorosa, Chiaojung Jillian Tsai, Margie Hunt:
Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency. 18-25 - Yang Lei, Yabo Fu, Joseph Harms, Tonghe Wang, Walter J. Curran, Tian Liu, Kristin Higgins, Xiaofeng Yang:
4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network. 26-33 - Wei Zhao, Bin Han, Yong Yang, Mark Buyyounouski, Steven L. Hancock, Hilary Bagshaw, Lei Xing:
Toward Markerless Image-Guided Radiotherapy Using Deep Learning for Prostate Cancer. 34-42 - Zhiyu Liu, Wenhao Jiang, Kit-Hang Lee, Yat-Long Lo, Yui-Lun Ng, Qi Dou, Varut Vardhanabhuti, Ka-Wai Kwok:
A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network. 43-51 - Qiming Yang, Hongyang Chao, Dan Nguyen, Steve B. Jiang:
A Novel Deep Learning Framework for Standardizing the Label of OARs in CT. 52-60 - Szu-Yeu Hu, Wei-Hung Weng, Shao-Lun Lu, Yueh-Hung Cheng, Furen Xiao, Feng-Ming Hsu, Jen-Tang Lu:
Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery. 61-69 - Jingjing Zhang, Shuolin Liu, Teng Li, Ronghu Mao, Chi Du, Jianfei Liu:
Voxel-Level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions. 70-77 - John Ginn, James Lamb, Dan Ruan:
Online Target Volume Estimation and Prediction from an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach. 78-85 - Dashan Jiang, Teng Li, Ronghu Mao, Chi Du, Yongbin Liu, Shuolin Liu, Jianfei Liu:
One-Dimensional Convolutional Network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning. 86-93 - Denis Prokopenko, Joël Valentin Stadelmann, Heinrich Schulz, Steffen Renisch, Dmitry V. Dylov:
Unpaired Synthetic Image Generation in Radiology Using GANs. 94-101 - Ge Ren, Wai Yin Ho, Jing Qin, Jing Cai:
Deriving Lung Perfusion Directly from CT Image Using Deep Convolutional Neural Network: A Preliminary Study. 102-109 - Jianhui Ma, Ti Bai, Dan Nguyen, Michael Folkerts, Xun Jia, Weiguo Lu, Linghong Zhou, Steve B. Jiang:
Individualized 3D Dose Distribution Prediction Using Deep Learning. 110-118 - Kibrom Berihu Girum, Gilles Créhange, Raabid Hussain, Paul Michael Walker, Alain Lalande:
Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy. 119-127 - Bilel Daoud, Ken'ichi Morooka, Shoko Miyauchi, Ryo Kurazume, Wafa Mnejja, Leila Farhat, Jamel Daoud:
Dose Distribution Prediction for Optimal Treamtment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma. 128-136 - Ryan Neph, Yangsibo Huang, Youming Yang, Ke Sheng:
DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy. 137-145 - Haitao Wu, Xiling Jiang, Fucang Jia:
UC-GAN for MR to CT Image Synthesis. 146-153 - Yang Lei, Tonghe Wang, Joseph Harms, Yabo Fu, Xue Dong, Walter J. Curran, Tian Liu, Xiaofeng Yang:
CBCT-Based Synthetic MRI Generation for CBCT-Guided Adaptive Radiotherapy. 154-161 - Rabia Haq, Alexandra Hotca, Aditya P. Apte, Andreas Rimner, Joseph O. Deasy, Maria Thor:
Cardio-Pulmonary Substructure Segmentation of CT Images Using Convolutional Neural Networks. 162-169
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