| Monday |
MO-K-DBRA-4: Data Science: Applications in Radiation Therapy
Feasibility of Using Machine Learning Techniques to Predict the Results of VMAT Patient-Specific QC Measurements |
Dal Granville |
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MO-K-DBRA-8: Data Science: Applications in Radiation Therapy
Volumetric Dose Prediction On Head and Neck Cancer Patients with a Novel Deep Learning Architecture: Hierarchically Densely Connected U-Net |
Dan Nguyen |
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MO-K-KDBRC-1: Advanced Computing Applications
Automated Standardization of Organ Labeling in Head and Neck Using Deep Learning |
Timothy Rozario |
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MO-K-KDBRC-2: Advanced Computing Applications
BEST IN PHYSICS (JOINT IMAGING-THERAPY): Deep Learning Mapping of CT to MRI for Longitudinal Tracking of Lung Tumors for MRI-Guided Radiotherapy |
Jue Jiang |
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MO-K-KDBRC-3: Advanced Computing Applications
Automated Detection of Vertebral Body Metastases for Fully-Automated Palliative Radiotherapy Using Transfer Learning |
Tucker Netherton |
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MO-K-KDBRC-4: Advanced Computing Applications
An Automated Method to Generate Patient-Specific Dose Distributions for Radiotherapy Using Deep Learning |
Xinyuan Chen |
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MO-K-KDBRC-6: Advanced Computing Applications
JACK KROHMER JUNIOR INVESTIGATOR COMPETITION WINNER: Selecting Predictive Genomic Biomarkers for Oropharyngeal Cancer Treatment Prediction by Use of Advanced Machine Learning Method |
Jian Wu |
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| Tuesday |
TU-AB-DBRB-5: Quantitative Imaging: Translation to Practice
Automatic Segmentation of the Prostate Gland On Planning CT Images Using Deep Neural Networks (DNN) |
Chang Liu |
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TU-D-DBRB-5: Quantitative Imaging: Ultrasound, CT, and PET/CT Applications
Automatic Liver and Tumor Segmentation Using Hierarchical Convolutional-Deconvolutional Neural Networks with Jaccard Distance |
Yading Yuan |
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TU-K-202-4: Computed Tomography I
Automated Segmentation of Malignant Pleural Mesothelioma Tumor On Computed Tomography Scans Using Deep Convolutional Neural Networks |
Eyjolfur Gudmundsson |
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| Wednesday |
WE-AB-KDBRC-10: Longitudinal and Functional Imaging
Predicting Lung Tumor Shrinkage During Radiotherapy Seen in a Longitudinal MR Imaging Study Via a Deep Learning Algorithm |
chuang wang |
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WE-FG-207-3: Computed Tomography II
Deep Learning for Automated Quantification of Tumor Phenotypes |
Ahmed Hosny |
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WE-FG-KDBRB1-8: MRI Guided Radiation Therapy
Deep Learning for Head and Neck Segmentation in MR: A Tool for the MR-Guided Radiotherapy |
Brian Anderson |
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WE-HI-KDBRB1-4: Tracking and Motion Management
Predicting Real-Time 3D Deformation Field Maps (DFM) Based On Volumetric Cine MRI (VC-MRI) and Artificial Neural Networks for On-Board 4D Target Tracking/Gating |
Wendy Harris |
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| Thursday |
TH-AB-KDBRB1-5: Point/Counterpoint Live Debate: Artificial Intelligence Will Soon Change the Landscape of Medical Physics Research and Practice
Super-Resolution 1H Magnetic Resonance Spectroscopic Imaging Utilizing Deep Learning |
Zohaib Iqbal |
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TH-AB-KDBRB1-6: Point/Counterpoint Live Debate: Artificial Intelligence Will Soon Change the Landscape of Medical Physics Research and Practice
Lung Nodule Malignancy Prediction by Combining Handcrafted Features and Deep Convolutional Neural Network |
Shulong Li |
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TH-AB-KDBRB1-7: Point/Counterpoint Live Debate: Artificial Intelligence Will Soon Change the Landscape of Medical Physics Research and Practice
A Deep Learning-Based Tumor Auto-Contouring Algorithm for Real-Time Tumor Tracking Using Linac-MR |
Jihyun Yun |
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TH-AB-KDBRB1-9: Point/Counterpoint Live Debate: Artificial Intelligence Will Soon Change the Landscape of Medical Physics Research and Practice
Using Machine Learning to Develop a Novel Error Detection Algorithm for HDR Brachytherapy |
David Sterling |
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TH-CD-207-2: Radiography and Fluoroscopy
Deep Learning in the Classification of Thoracic Radiographic Views to Enable Accurate and Efficient Clinical Workflows |
Jennie Crosby |
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TH-CD-KDBRC-8: Registration and Segmentation
Atrous Convolution and Spatial Pyramid Pooling for More Accurate Tumor Segmentation in MR Images |
Kuo Men |
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TH-CD-KDBRC-9: Registration and Segmentation
A Deep Learning Based Auto Segmentation for H&N Organs On Treatment Planning CT Images |
Shijun Sun |
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TH-EF-202-9: Cone-beam Computed Tomography
Noise Subtraction for CT Images Acquired at Multiple Dose Levels Using a Deep Convolutional Neural Network |
Andrew Missert |
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TH-EF-202-4: Cone-beam Computed Tomography
CBCT Projection-Domain Scatter Correction with a Residual Convolutional Neural Network |
Yusuke Nomura |
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TH-EF-KDBRB1-9: MRI in Treatment Planning
Patient-Specific Synthetic CT Generation for MRI-Only Prostate Radiotherapy Treatment Planning |
Xiaofeng Yang |
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