Conference on Machine Intelligence

in Medical Imaging

September 22-23

Society for Imaging Informatics in Medicine | siim.org | @SIIM_Tweets I #CMIMI19

Hilton Austin Hotel

SUNDAY, SEPTEMBER 22

7:00 am - 7:45 am Registration & Continental Breakfast | Governor’s Ballroom CD Foyer, 4th Floor

7:45 am - 8:00 am Welcome Remarks Bradley J. Erickson, MD, PhD, CIIP, FSIIM, Mayo Clinic Paul G. Nagy, PhD, CIIP, FSIIM, Johns Hopkins University School of Medicine, Technology Innovation Center

8:00 am - 9:00 am Simplifying Machine Learning to Enable Citizen Scientists Shez Partovi, MD, Amazon Web Services

9:00 am - 10:25 am Scientific Oral Presentations - Machine Learning Algorithms & Toolkits Chair: Bradley J. Erickson, MD, PhD, CIIP, FSIIM, Mayo Clinic A Comprehensive Deep Learning Model for Automated Classification of Chest Radiographs Sehyo Yune, MD, MPH, MBA, Massachusetts General Hospital Adaptive Self-Balancing Framework for Multi-Class Segmentation Kenneth Philbrick PhD, Mayo Clinic Adaptive Transfer Learning between Two Detector Types of Mammograms via Generative Adversarial Network Myeongchan Kim, MD, Massachusetts General Hospital An Explainable Deep-Learning for Detection and Classification of Pulmonary Tuberculosis YiRang Shin, MS, Massachusetts General Hospital Auto-AI Method That Self-learns by Looking at a Minimal Number of Medical Images Briana Malik, Parkland High School DeepNeuro: An Open-source Deep Learning Toolbox for Neuroimaging Ken Chang, MSE, Athinoula A. Martinos Center for Biomedical Imaging

10:25 am - 10:45 am Break

ID: HiltonMeetings

PW: CMIMI2019

SUNDAY, SEPTEMBER 22

10:45 am - 12:15 pm Scientific Oral Presentations - Clinical Applications Chair: Paul G. Nagy, PhD, CIIP, FSIIM, Johns Hopkins University School of Medicine, Technology Innovation Center An EEG-fNIRS Hybridization Technique in the Four-Class Classification of Alzheimer's Disease Pietro A. Cicalese, PhD Candidate; Rihui Li, PhD Candidate, University of Houston Automated Grading of Knee Osteoarthritis by Using Convolutional Neural Networks Yee Seng Ng, MD, UT Southwestern Automated Segmentation of Indirect Immunofluorescence Images using Fast Fuzzy C-Means Algorithm for Keypoint based Differentiation of Homogenous and Speckled Shapes Kanchana Devanathan, PhD Candidate, Indian Institute of Technology Madras Automatic Glioma Volume and Bi-directional Measurement in MRI using Deep Learning Ken Chang, MSE, Athinoula A. Martinos Center for Biomedical Imaging Automatic Segmentation and Longitudinal Tracking of Brain Metastatic Lesions on MRI Ken Chang, MSE, Athinoula A. Martinos Center for Biomedical Imaging Classifying Early Stage Cardiac Amyloid from Echocardiogram Images using Deep Learning Qiao Huang, PhD, Mayo Clinic Rochester DeepCAT: Deep Computer-Aided Triage of Mammograms Paul H. Yi, MD, Johns Hopkins University School of Medicine

12:30 pm - 1:45 pm Lunch | Governor’s Ballroom AB, 4th Floor

2:00 pm - 2:45 pm Assessment Methods and Regulatory Frameworks for Quantitative Imaging and Machine Learning Tools Nicholas A. Petrick, PhD, U.S. Food and Drug Administration

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SUNDAY, SEPTEMBER 22

2:45 pm - 4:10 pm Scientific Oral Presentations - Datasets, Infrastructure to Support Machine Learning, Non-pixel-based Machine Learning Chair: Eliot L. Siegel, MD, FSIIM, University of Maryland School of Medicine, VA Maryland Health Care System Distributed Single-model Training in Inter-institutional Collaboration without Exposing Each Dataset Jinserk Baik, PhD, Massachusetts General Hospital How Accurate is Human Segmentation? A Comparison of 14 Human Tracers Performing Whole Abdomen Segmentation of 27 Organs Alexander D. Weston, Mayo Clinic Intelligent Search of Pneumonia in Dictated Radiology Reports Frederic H. Lachmann, PhD, Intelerad Synchronous Distributed Deep Learning for Medical Imaging Simukayi T. Mutasa, MD, Columbia University Medical Center Thoracic Radiograph Workflow to Support Machine Learning Methods for Pneumothorax Detection and Localization Jennie S.M. Crosby, University of Chicago Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization Yu Zhang, University of Kentucky

4:10 pm - 4:25 pm Break

4:25 pm - 5:40 pm Driving AI Adoption in Clinical Practice Co-chairs: Bradley J. Erickson, MD, PhD, CIIP, FSIIM, Mayo Clinic Eliot L. Siegel, MD, FSIIM, University of Maryland School of Medicine, VA Maryland Health Care System Jeffrey Chang, MD, Co-Founder, Rad AI Brad Genereaux, Medical Imaging Alliance Manager, Nvidia Karen Holzberger, Senior Vice President & General Manager, Healthcare Diagnostics, Nuance Communications Morris Panner, JD, Chief Executive Officer, Ambra Health Jeffrey Sorenson, Chief Executive Officer TeraRecon

SUNDAY, SEPTEMBER 22

5:45 pm - 6:45 pm Wine, Popcorn & Posters Networking Event | Governor’s Ballroom CD Foyer, 4th Floor A Platform for Searching Clinical Documents and Medical Images to Facilitate AI/ML Development Bing Zhu, PhD, University of California, Los Angeles Anomaly Detection in Noisy Chest X-rays Using an Ensemble Deep Convolutional Neural Network Nan Zhang, PhD, Mayo Clinic Automated CT Pelvic Fracture Severity Grading with Deep Learning: Association with Clinical Outcomes David Dreizin, MD, University of Maryland School of Medicine and R. Adams Cowley Shock Trauma Center Deep Learning for Automatic Patient-Specific Adhered Sickle Cell Image Segmentation Niksa Praljak, Cleveland State University Differentiating Masslike Colon Diverticular Disease Versus Cancer using Textural Analysis in CT Images Metab Alkubeyyer, MD, King Saud University Medical City Differentiation of Tumefactive Multiple Sclerosis, Gioblastomas, Metastases and Primary Central Nervous System Lymphomas using Magnetic Resonance Imaging and Convolutional Neural Networks: A Pilot Study Gian Marco Conte MD, PhD, Mayo Clinic Gastric Polyp Detection using Deep Convolutional Neural Network Siddharth Jindal, MTech, IIT Roorkee Improved Low-Dose Chest CT Denoising with Capsule Networks Praneeth Sadda, MD, Tulane University Quantification of CT Body Fat Compartment using Automated Method Metab Alkubeyyer, MD, King Saud University Medical City Segmentation of Liver Lesions on Axial Computed Tomography images using Transfer Learning Barrett J. Anderies, Mayo Clinic SQLite4radiomics - Integrating Radiomics Extraction Into PACS Ivan Zhovannik, MSc, Radbound University Medical Center, Maastricht University Medical Center

MONDAY, SEPTEMBER 23

7:00 am - 7:45 am Registration & Continental Breakfast | Governor’s Ballroom CD Foyer, 4th Floor

7:45 am - 8:45 am Deep Learning for Medical Image Interpretation Pranav Rajpurkar, PhD Candidate, Stanford University

8:45 am - 10:00 am Scientific Oral Presentations - Machine Learning Algorithms & Toolkits Chair: Paul G. Nagy, PhD, CIIP, Johns Hopkins University School of Medicine, Technology Innovation Center Evaluating Generalization of Deep Learning Models on Cardiac MRI Image Segmentation Mai H. Nguyen, PhD, University of California, San Diego Genetic Deep Learning for Lung Cancer Screening H. Michael Park, Innovation Dx, Inc. GrayNet: A Versatile Base Model for Practical Deep Learning CT Applications Myeongchan Kim, MD, Massachusetts General Hospital Overcoming Data Variability Challenges to Federated Deep Learning for Medical mage Analysis Niranjan Balachandar, Stanford University Physics-Informed Deep Neural Nets for Super-Resolution and Denoising of Image Space 4D-Flow MRI Roshan M. D'Souza, PhD, University of Wisconsin-Milwaukee

10:00 am - 10:20 am Break

10:20 am - 11:35 am Scientific Oral Presentations - Clinical Applications Chair: Eliot L. Siegel, MD, FSIIM, University of Maryland School of Medicine, VA Maryland Health Care System Estimation of Hormone Receptor Expression and Tumor Morphology in Ductal Carcinoma in Situ (DCIS) from H&E Stained Slides Dmytro S. Lituiev, PhD, University of California, San Francisco Focal Loss for Classification and Localization of Retrievable IVC filter on Radiographs Judy W. Gichoya, MD, MS, Emory University Leveraging Deep Learning Artificial Intelligence in Detecting the Orientation of Chest X-ray Images Khaled Younis, PhD, GE Healthcare

MONDAY, SEPTEMBER 23

Machine Intelligence for Blood Testing using Magnetic Levitational Imaging Cytometry Nathan Dass, Stanford University

Simulated Brain MRI Images for Deep Learning: Potential Alternative to GANs Paul H. Yi, MD, Johns Hopkins University School of Medicine

Using Deep Learning to Estimate Penumbra for Acute Ischemic Stroke Patients with DWI and ASL Yuan Xie, Stanford University

11:45 am - 1:00 pm Lunch | Governor’s Ballroom AB, 4th Floor

1:15 pm - 2:15 pm What do Competitions Need to do to Build Confidence in Imaging AI?

Chair: Bennett A. Landman, PhD, Vanderbilt University

Raym Geis, MD, FSIIM, ACR Data Science Institute, National Jewish Health Jack Spencer, PhD, Massachusetts Institute of Technology Julian Rivera, Husch Blackwell LLP

2:15 pm - 3:15 pm SIIM-ACR Pneumothorax Challenge Awards Presentation

Laura Brink, American College of Radiology Julia Elliott, Kaggle Steve G. Langer, PhD, CIIP, FSIIM, Mayo Clinic George Shih, MD, MS, Weill Cornell Medical Center

3:15 pm - 3:45 pm Closing Remarks

Bradley J. Erickson, MD, PhD, CIIP, FSIIM, Mayo Clinic Eliot L. Siegel, MD, FSIIM, University of Maryland School of Medicine, VA Maryland Health Care System

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