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Taxonomy: IM/TH- Formal Quality Management Tools: Machine Learning
| MO-AB-221AB-4 | Improving Low-Dose Cone Beam CT Image Quality Via Convolutional Neural Network N Yuan1,2, S Rao3 , B Dyer3 , S Benedict3 , Y Kang1 , J Qi2 , Y Rong*3 , (1) Northeastern Univerisity, Shenyang (2) Univerisity of California, Davis, Davis, CA (3) UC Davis Cancer Center, Sacramento, CA |
| MO-AB-303-7 | Order-Graph Regularized Sparse Dictionary Learning for Unsupervised Multi-Needle Detection in 3D Ultrasound Images Y Zhang , Y Lei , J Jeong , Z Tian , Y Liu , T Wang , T Liu , A Jani , W Curran , P Patel , X Yang*, Emory Univ, Atlanta, GA |
| MO-E115-GePD-F2-3 | Predicting Acute-Phase Weight Loss Based On CT Radiomics and Dosiomics in Lung Cancer Patients Treated with Radiotherapy S Lee , P Han , R Hales , K Voong , T McNutt , J Lee*, Johns Hopkins University, Baltimore, MD |
| MO-I345-GePD-F2-5 | VMAT Plan Complexity Feature Analysis for Predicting Quality Assurance Outcomes Using Forests of Extremely Randomized Decision Trees P Wall1*, J Fontenot1,2 , (1) Louisiana State University, Baton Rouge, LA, (2) Mary Bird Perkins Cancer Center, Baton Rouge, LA |
| MO-I345-GePD-F7-5 | Retaining Novel Cases to Improve Model Robustness in a Case Based Reasoning Workflow for Radiation Therapy Planning Y Sheng1*, J Zhang1 , C Wang1 , F Yin1 , Q Wu1 , Y Ge2 , (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC |
| MO-J430-CAMPUS-F2-2 | Deep Learning in Medical Physics: Reality Or Noise? G Valdes*, M Romero-Calvo , T Solberg , Y Interian , UCSF Comprehensive Cancer Center, San Francisco, CA |
| MO-J430-CAMPUS-F2-4 | Machine Learning Method to Automate Structure Name Mapping W Sleeman IV1,3*, J Nalluri1,3 , S Khajamoinuddin2 , P Ghosh2 , M Hagan1,3 , J Palta1,3 , R Kapoor1,3 , (1) Virginia Commonwealth University, Department of Radiation Oncology, Richmond, VA (2) Virginia Commonwealth University, Department of Computer Science, Richmond, VA (3) National Radiation Oncology Program, Department of Veteran Affairs, Richmond, VA |
| MO-J430-CAMPUS-F2-5 | Intelligent Synthetic CT Generation Based On CBCT Images Via Unsupervised Deep Learning L Chen*, X Liang , C Shen , S Jiang , J Wang , UT Southwestern Medical Center, Dallas, TX |
| MO-J430-CAMPUS-F3-4 | Impact of Cardiac Substructure Dose for Modeling Radiation Toxicity in the Heart J Niedzielski*, X Wei , T Xu , D Gomez , Z Liao , J Bankson , S Lai , L Court , J Yang , University of Texas-MD Anderson Cancer Center, Houston, TX |
| PO-GePV-I-9 | Unsupervised Classification Routine to Correlate Nonlinearly Related Multiple Images: An Example for CT/CBCT Lung Images Normalization A Chu1*, J Kim2 , Z Xu3 , S Ryu4 , W Liu5 , W Tome6 , (1)(2)(3)(4) Stony Brook University, Stony Brook, NY, (5) Yale Univ. School of Medicine, New Haven, CT, (6) Montefiore Medical Center, Bronx, NY |
| PO-GePV-I-13 | Augmentation of MRI Multi-Sequence Radiomics Data to Improvebrain Tumor Classification K Ogden1*, N Salastekar1 , D LaBella1 , A Chakraborty1 , E Oakes2 , R Mangla1 , (1) SUNY Upstate Medical Univ, Syracuse, NY, (2) Syracuse University, Syracuse, NY |
| PO-GePV-I-16 | Image Acquisition Technique for Nuclear Medicine Using Deep Profile Learning M Choi*, D Yoon , M Kim , T Suh , The Catholic University of Korea, College of MedicineSeoul |
| PO-GePV-M-4 | Utilizing Quantitative Local Trajectory Method to Online Analyse Intrafraction Prostate Motion Y Gao*, B Zhao , X Qi , X Gao , Peking University First Hospital, Beijing |
| SU-E-221AB-2 | A Relational Autoencoder for Retrieving Similar Patients in Radiotherapy Treatment Planning K Wang*, X Gu , M Chen , W Lu , UT Southwestern Medical Center, Dallas, TX |
| SU-E-221AB-4 | Machine Learning in IMRT Plan Evaluation A Roy1*, D Cutright2 , M Gopalakrishnan3 , B Mittal3 , (1) The University of Texas at San Antonio, San Antonio, TX, (2) University of Chicago Medicine, Chicago, IL, (3) Northwestern Memorial Hospital, Chicago, IL |
| SU-E-225BCD-2 | A Benchmark for Breast Ultrasound Image Computer-Aided Diagnosis E Zhang1*, J Li2 , S Seiler3 , M Chen4 , W Lu5 , X Gu6 , (1) UT Southwestern Medical Center, Dallas, TX, (2) Guangdong General Hospital, Guangzhou, China, (3) UT Southwestern Medical Center, Dallas, TX, (4) UT Southwestern Medical Center, Dallas, TX, (5) UT Southwestern Medical Center, Dallas, TX, (6) UT Southwestern Medical Center, Dallas, TX |
| SU-E-303-5 | Transfer Learning From MR to CT for Prostate Segmentation Using 2.5D Unet Yucheng Liu1*, Yulin Liu2 , Michael Liu1, Rami Vanguri1, Joe Stember1, Jonathan Shoag3, Sachin Jambawalikar1 , (1) Columbia University Medical Center, New York, NY, (2) Chung Yuan Christian University, Taoyuan, Taiwan, (3)Weill Cornell Medicine, New York, NY |
| SU-E-SAN4-1 | A Graphical User Interface (GUI) Toolkit for Treatment Plan Quality Analysis in Right Lung SBRT A Brito Delgado1*, K Rasmussen2 , K Kauweloa3 , T Medrano Pesqueira4 , D Cohen5 , T Eng6 , N Kirby7 , D Saenz8 , Z Shi9 , S Stathakis10 , N Papanikolaou11 , A Gutierrez12 , (1) University of Kansas Hospital, Overland Park, KS, (2) University of Texas HSC SA, San Antonio, TX, (3) University of Kansas Medical Center, Overland Park, KS, (4) Centro Estatal de Oncologia, Hermosillo, Sonora, Mexico ,(5) Jefferson Health New Jersey, Sewell, ,(6) Emory University, Atlanta, ,(7) University of Texas HSC SA, San Antonio, TX, (8) University of Texas HSC SA, San Antonio, TX, (9) University of Texas HSC SA, San Antonio, TX, (10) University Of Texas Health, San Antonio, TX, (11) University of Texas HSC SA, San Antonio, TX, (12) Miami Cancer Institute, Miami, FL |
| SU-F-221AB-1 | A Patient-Independent CT Intensity Correction Method Using Generative Adversarial Networks (GAN) for Single X-Ray Based Tumor Localization R Wei1*, F Zhou1 , B Liu1 , X Bai1 ,Q Wu2 , (1) Image Processing Center, Beihang University, Beijing, ,(2) Duke University Medical Center, Durham, NC |
| SU-F-303-4 | K-Nearest Neighbor Model-Based Approach for Classification of TI-RADS Class-4 Thyroid Nodules W Lu* , T Wang , W Lu , L Shi , K Hou , H Zhao , J Qiu , Taishan Medical University, Taian, Shandong |
| SU-F-304-6 | Known-Component Metal Artifact Reduction (KC-MAR) for Intraoperative Cone-Beam CT in Spine Surgery: A Clinical Pilot Study X Zhang1*, A Uneri1 , S Doerr1 , J Stayman1 , C Zygourakis2 , S Lo2 , N Theodore2 , J Siewerdsen1 , (1) Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, (2) Department of Neurosurgery, Johns Hopkins Medicine, Baltimore, MD |
| SU-F-304-8 | Accurate CBCT Prostate Segmentation Aided by CBCT-Based Synthetic MRI Y Lei , S Tian , Z Tian , T Wang , Y Liu , X Jiang , T Liu , A Jani , W Curran , P Patel , X Yang*, Emory Univ, Atlanta, GA |
| SU-F-SAN2-1 | CBCT Image Quality Augmentation Using Deep Learning Models: A Comparison Study Y Zhao1*, Z Jiang2 , X Teng1 , L Ren2 , (1) Duke Kunshan University, Kunshan,(2) Duke Univeristy, Durham, NC |
| SU-F-SAN2-4 | Feasibility of CT-Only 3D Dose Prediction for VMAT Prostate Plans Using Deep Learning S Willems1*, W Crijns1 , E Sterpin1,2 , K Haustermans1 , F Maes1 , (1) KULeuven (2) UCLouvain |
| SU-G300-SPS-F4-4 | Comparison of Classifier Performance for Several Machine Learning Classification Tasks for Computer-Aided Diagnosis of Breast Cancer Using DCE-MRI M Vieceli1*, K Drukker2 , J Papaioannou2 , A Edwards2 , H Abe2 , M Giger2 , H Whitney1,2 , (1) Wheaton College, Wheaton, IL, (2) University of Chicago, Chicago, IL, |
| SU-G300-SPS-F4-7 | Prediction of Acute Xerostomia in Nasopharyngeal Cancer for Radiotherapy Using 3D Convolutional Neural Network Y LIU1*, X CHEN2 , s Huang3 , H SHI4 , H ZHOU5 , H CHANG6 , Y XIA7 , X Yang8 , (1) School of Software Engineering, South China University of Technology, Guangzhou, ,(2) School of Software Engineering, South China University of Technology, Guangzhou,(3) State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, (4) School of Software Engineering, South China University of Technology, Guangzhou, ,(5) the 74th Group Army Hospital of the Chinese People's Liberation Army, Guangzhou, ,(6) SYSUCC, Guangzhou, ,(7) SYSUCC, Guangzhou, ,(8) Sun Yat-Sen University Cancer Center (SYSUCC), Guangzhou City |
| SU-G300-SPS-F4-9 | Validation of Production Standardizing Radiation Therapy Structures Names by the Content-Based Standardizing Nomenclatures (CBSN) in Radiation Oncology X MAI1,2*, S HUANG1,2 , S Huang1 , Y XIA1 , X HUANG1 , X Yang1 , (1) Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China. (2) Xinhua College of Sun Yat-sen University, Guangzhou, Guangdong, 510520,China. |
| SU-I300-GePD-F8-5 | The Feasibility of MVCT-Based Radiomics for Delta-Radiomics in Head and Neck Cancer K Abe1,2*, N Kadoya2 , S Tanaka2 , Y Nakajima1,2 , S Hashimoto1 , T Kajikawa2 , K Karasawa1 , K Jingu2 , (1) Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan,(2) Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendal, Japan |
| SU-I400-GePD-F5-6 | 3D Dose Prediction Model for Head and Neck Cancer Patients by Combining Field Geometry Information with Patient Images E Czeizler*, M Hakala , S Basiri , E Kuusela , Varian Medical Systems Finland, Helsinki, 18 |
| SU-I400-GePD-F8-3 | Image Synthesis in Multi-Contrast MRI with Deep Convolutional Generative Adversarial Networks D Kawahara1*, S Ozawa1 , A Saito1, K Miki1, Y Murakami1, T Kimura1, Y Nagata1, (1) Hiroshima University, Hiroshima |
| SU-I430-GePD-F6-2 | Machine Learning Based Region of Interest Optimization Framework for Optical Surface Monitoring System: A Feasibility Study T Chen*, D Barbee , P Cohen , K Du , New York University, New York, NY |
| SU-I430-GePD-F9-4 | Quantitative Evaluation of Image Quality in Low Dose CT Images Obtained by Deep Learning D Lee*, H Kim , |
| SU-J400-CAMPUS-F1-2 | Combined Use of Gray Matter Volume and Quantitative Susceptibility Mapping to Predict Early Alzheimers Disease Using a Machine Learning-Based Optimized Combination-Feature Set HK Kim1 , HY Rhee2 , CW Ryu3 ,GH Jahng3*, (1) Radiology, Kyung Hee University Hospital, Seoul,Korea ,(2) Neurology, Kyung Hee University Hospital at Gangdong, Seoul,Korea ,(3) Radiology, Kyung Hee University Hospital at Gangdong, Seoul,Korea |
| SU-K-221CD-1 | A Deep-Learning Based Lower-Dose CT Simulation Technique in Image Domain H Gong*, S Leng , C McCollough , L Yu , Mayo Clinic, Rochester, MN |
| SU-K-303-7 | Projection-Domain Convolutional Neural Network Denoising for X-Ray Phase-Contrast Micro Computed Tomography E Shanblatt*, A Missert , B Nelson , S Leng , C McCollough , Mayo Clinic, Rochester, MN |
| TH-A-303-9 | MRI Radio Frequency Power Amplifier Linearization with Pre-Distortion Based On Artificial Neural Network W Lu*, W Lu , L Shi , K Hou , H Zhao , J Qiu , Department of Radiology, Taishan Medical University, Taian, Shandong |
| TH-A-SAN2-7 | Deep-Learning Based CBCT Image Correction for CBCT-Guided Adaptive Radiation Therapy J Harms1*, Y Lei1 , T Wang1 , R Zhang1 , J Zhou1 , X Dong1 , P Patel1 , K Higgins1 , X Tang2 , W Curran1 , T Liu1 , X Yang1 , (1) Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, (2) Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA 30322 |
| TH-A-SAN2-10 | Adaptive Margins with An Early Warning System for Motion-Tracking Errors in Liver SBRT M Liu1*, A Ross2 , J E Cygler1,3,4 , E Vandervoort1,3,4 (1) Department of Physics, Carleton University, Ottawa, ON, Canada (2) Department of Physics, McGill University, Montreal, QC, Canada (3) Department of Medical Physics, The Ottawa Hospital Cancer Centre, Ottawa, ON, Canada (4) Department of Radiology, University of Ottawa, Ottawa, ON, Canada |
| TH-C-SAN2-2 | Combination of Multiple Neural Networks Using Transfer Learning and Extensive Geometric Data Augmentation for Assessing Cellularity Scores in Histopathology Images J Beckmann*, K Popovic , Rose-Hulman Institute of Technology, Terre Haute, IN |
| TU-AB-225BCD-3 | Dual Energy Bone Suppression Using Neural Networks M Haytmyradov1*, F Cassetta1, R Patel1, M Surucu1, H. Mostavafi2, J Roeske1, (1) Loyola University Medical Center, Maywood, IL, USA (2) Varian Medical Systems, Palo Alto, CA, USA |
| TU-AB-SAN2-3 | A Deep Learning Method for Xerostomia Prediction in Head-And-Neck Radiotherapy K Men*, H Geng , H Zhong , Y Fan , A Lin , Y Xiao , University of Pennsylvania, Philadelphia, PA 19104, USA |
| TU-AB-SAN2-4 | Multi-Branch Convolutional Neural Network Combines Unregistered PET and CT Images for Head & Neck Cancer Outcome Prediction A Diamant*, A Chatterjee , M Vallieres , G Shenouda , J Seuntjens , McGill University Health Centre, Montreal, QC |
| TU-C1000-GePD-F2-5 | Synthetic CT Generation Using Unpaired Images in a CycleGAN with Identity Loss Z Sun1 , S Baek1 , S Yaddanapudi1 , J St-Aubin1*, University of Iowa, Iowa City, IA |
| TU-C1000-GePD-F6-2 | Building Robust Machine Learning Models for Colorectal Cancer Risk Prediction B Nartowt1*, G Hart2 , W Muhammad3 , Y Liang4 , J Deng5 , (1) Yale/New Haven Hospital, New Haven, CT, (2) Yale University, New Haven, CT, (3) Yale School of Medicine, Yale University, New Haven, CT, (4) Medical College of Wisconsin, Milwaukee, WI, (5) Yale Univ. School of Medicine, New Haven, CT |
| TU-C1000-GePD-F6-3 | Decision Trees Identifying Factors Affecting Tumor Response to Chemo-Radiotherapy in Head and Neck Cancer Evaluated for Tumor Burden M Surucu1*, I Mescioglu2 , A Block1 , B Emami1 , J Roeske1 , (1) Loyola University Medical Center, Maywood, IL, (2) Lewis University, Romeoville |
| TU-C1000-GePD-F6-6 | Machine Learning Based Method for Peer Review Rounds Case Prioritization L Conroy*, C McIntosh , T Purdie , The Princess Margaret Cancer Centre - UHN, Toronto, ON |
| TU-C1030-GePD-F9-6 | What Image Features Are Good for Correlation-Based Tracking Algorithms Used for Soft Tissue Monitoring in X-Ray Imaging A Jeung*, L Zhu , H Mostafavi , J van Heteren , Varian Medical Systems, Palo Alto, CA |
| TU-C930-GePD-F9-2 | Calibrator Recognition of Cone-Beam Image for Geometric Correction Q Ling1*, X Duan2 , J Ma3 , J Huang4 , S Huang5 , J Cai6 , L Zhou7 , Y Xu8 ,(3) UT Southwestern Medical Center, Dallas, TX, (1-2,4-8) Southern Medical University,Guangzhou |
| TU-C930-GePD-F9-5 | Transfer Learning of a Convolutional Neural Network for CBCT Projection-Domain Scatter Correction with Different Scan Conditions Y Nomura1*, Q Xu2,3 , H Shirato2,4 , S Shimizu2,5 , L Xing2,6 , (1) Department of Radiation Oncology, Graduate School of Medicine, Hokkaido University, Sapporo, Japan, (2) Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Sapporo, Japan, (3) Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China, (4) Department of Radiation Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan, (5) Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan, (6) Department of Radiation Oncology, Stanford University, Stanford, CA |
| TU-E-SAN4-0 | The Integration of AI and Machine Learning in Medical Physics Applications V Kearney1*, M Chan2*, C Cardenas3*, (1) University of California San Francisco, San Francsico, CA, (2) Memorial Sloan Kettering Cancer Center, Basking Ridge, NJ, (3) University of Texas MD Anderson Cancer Center, Houston, TX |
| TU-F115-GePD-F5-1 | Artificial Intelligence-Based Dose-Guided Patient Positioning for Prostate Cancer Online Adaptive Radiotherapy X Zhang1*, (1) West China hospital of Sichuan university, Chengdu, Sichuan |
| TU-F115-GePD-F9-1 | Automatic Multi-Organ Segmentation On Female Pelvic CT with Dense V-Network Q Wu*, Peoples Liberation Army General HospitalBeijing |
| TU-L-304-8 | Prognosis Prediction with Homology-Based Radiomic Features Quantifying the Lung Tumor Malignancy in CT-Based Radiomics S Tanaka1*, N Kadoya1 , T Kajikawa1 , K Abe1, 2, S Dobashi3 , K Takeda3 , K Nakane4 , K Jingu1 , (1) Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan, (2) Department of Radiology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan, (3) Course of Radiological Technology, Health Sciences, Tohoku University Graduate School of Medicine, Sendai, Japan, (4) Department of Medicine, Osaka University Graduate School of Medicine, Osaka, Japan |
| WE-AB-221AB-7 | Noise Subtraction for Dual Energy CT Images Using A Deep Convolutional Neural Network A Missert*, L Yu , S Leng , C McCollough , Mayo Clinic, Rochester, MN |
| WE-AB-225BCD-3 | A Composite Deep Learning Architecture for the Joint Prediction of Local Control and Radiation Pneumonitis in Radiotherapy for Non-Small Cell Lung Cancer Patients S Cui*, Y Luo , H Tseng , R Ten Haken , I El Naqa , University of Michigan, Ann Arbor, MI |
| WE-AB-225BCD-4 | Convolutional Neural Network for Centroiding and Depth-Of-Interaction Localization in PET A LaBella*, W Zhao , AH Goldan , Stony Brook University, Stony Brook, NY |
| WE-AB-225BCD-5 | Harmonization of Radiomic Features of Breast Lesions Extracted From DCE-MRI Across Two Populations H Whitney1,2*, H Li1 , Y Ji1,3 , A Edwards1 , J Papaioannou1 , P Liu3 , M Giger1 , (1) University of Chicago, Chicago, IL, (2) Wheaton College, Wheaton, IL,(3) Tianjin Medical University Cancer Institute and Hospital, Tianjin, China |
| WE-AB-225BCD-12 | Musculoskeletal Tumor Classification On T2-Weighted MRI Using Probability Fusion Convolutional Neural Network and Support Vector Machine L Chen*, S Fisher , A Rodriguez , M Folkert , A Chhabra , S Jiang , J Wang , UT Southwestern Medical Center, Dallas, TX |
| WE-C1030-GePD-F2-3 | Multitask-Based Supervised Deep Learning Using Contrast-Enhanced CT (CECT) Images for Hepatocellular Carcinoma (HCC) Intrahepatic Progression Risk Analysis L Wei1*, D Owen2 , M Mendiratta-Lala3 , B Rosen2 , K Cuneo2 , T Lawrence2 , R Ten Haken2 , I El Naqa2 , (1) Applied Physics Program, University of Michigan, Ann Arbor, MI, (2) Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, (3) Department of Radiology, University of Michigan, Ann Arbor, MI |
| WE-C1030-GePD-F5-1 | A Deep Learning Based Auto-Segmentation Method for Radiation Therapy of Head and Neck Cancer A Amjad1*, Z Chen2 , M Awan1 , M Shukla1 , C Yang2 , Q Zhou2 , X Li1 , (1) Medical College of Wisconsin, Milwaukee, WI, (2) Manteia Medical Technologies, Milwaukee, WI |
| WE-C1030-GePD-F6-3 | Generative Adversarial Network for Low Dose CT Denoising and Enhancement B Ye1 , X Qi2 , S Tan1*(1) Huazhong University of Science & Technology, Wuhan, China (2) UCLA School of Medicine, Los Angeles, CA |
| WE-FG-304-10 | Creation of An Ultra-Realistic EXtended Multi-Contrast ANthropomorphic (XMAN) Digital Phantom Using Cycle-Generative Adversarial Network (Cycle-GAN) Y Chang1*, F Yin2 , L Ren3 , (1) Duke University Medical Center, Durham, NC, (2) Duke University Medical Center, Durham, NC, (3) Duke University Medical Center, Cary, NC |
| WE-HI-303-5 | Support Vector Machine in the Differential Diagnosis of Benign and Malignant Thyroid Nodules T Wang*, L Shi , W Lu , J Qiu , W Lu , Taishan Medical University, Taian, Shandong |
| WE-HI-SAN2-7 | Stopping Power Map Estimation From Cone-Beam CT Using Deep Learning for CBCT-Guided Adaptive Radiation Therapy J Harms*, Y Lei , T Wang , B Ghavidel , W Stokes , T Liu , W Curran , J Zhou , M McDonald , X Yang , Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322 |
| WE-J-301-3 | Accurate and Instant Prediction of Electron Cutout Factor by An Efficient Residual Neural Network (ResNet) Model C He1 , L Lu2 , T Zhu3 , D Nie4 , S Chang5 , D Shen6 , J Lian7*, (1) Duke University, Durham, ,(2) The University of North Carolina at Chapel Hill, Chapel Hill, NC, (3) Univ of North Carolina at Chapel Hill, Chapel Hill, NC, (4) UNC Chapel Hill, Chapel Hill, ,(5) UNC School of Medicine, Chapel Hill, NC, (6) University of North Carolina at Chapel Hill, Chapel Hill, ,(7) Univ North Carolina, Chapel Hill, NC |
| WE-J-303-3 | A Multi-Layer Perception Based Method for Thyroid Imaging Reporting and Data System Class-4 Thyroid Nodules Diagnosis T Wang*, W Lu , L Shi , J Qiu , K Hou , H Zhao , W Lu , Taishan Medical University, Taian, Shandong |
