Invited Talk by Prof. Hayit Greenspan

Deep Learning in Medical Imaging: Solving the Data Augmentation Challenge for Enhanced-Value Radiology Reporting

Invited Talk by Prof. Alison Noble

Poster Session + Lunch

Deep semi-supervised segmentation with weight-averaged consistency targets. Christian Perone*, NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal; Julien Cohen-Adad, NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal.

Handling Missing Annotations for Semantic Segmentation with Deep ConvNets. Olivier Petit*, Visible Patient; Nicolas Thome, CNAM, Paris; Arnaud Charnoz, Visible Patient; alexandre hostettler, IRCAD France; Luc Soler, IRCAD

A Unified Framework Integrating Recurrent Fully-convolutional Networks and Optical Flow for Segmentation of Left Ventricle in Echocardiography. Data Mohammad Jafari*, University of British Columbia; Hany Girgis, Vancouver General Hospital; Zhibin Liao, The University of British Columbia; Delaram Behnami, UBC; Amir Abdi, University of British Columbia; Hooman Vaseli, University of British Columbia; Christina Luong, Vancouver General Hospital; Robert Rohling, University of British Columbia; Ken Gin, Vancouver General Hospital; Terasa Tsang, Vancouver General Hospital; Purang Abolmaesumi, The Univ. of British Columbia

TreeNet: Multi-Loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees. Mengliu Zhao*, Simon Fraser University; Ghassan Hamarneh, Simon Fraser University

3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation. Zhou He*, The Hong Kong University of Science and Technology; SIQI BAO, HKUST; Albert Chung, Hong Kong University of Science and Technology

Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography. Delaram Behnami*, UBC; Christina Luong, Vancouver General Hospital; Hooman Vaseli, University of British Columbia; Amir Abdi, University of British Columbia; Hany Girgis, Vancouver General Hospital; Dale Hawley, Vancouver General Hospital; Robert Rohling, University of British Columbia; Ken Gin, Vancouver General Hospital; Purang Abolmaesumi, The Univ. of British Columbia; Terasa Tsang, Vancouver General Hospital

MTMR-Net: Multi-Task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis. Lihao Liu*, The Chinese University of Hong Kong; Qi Dou, The Chinese University of Hong Kong; Hao Chen, The Chinese University of Hong Kong; Iyiola Olatunji, Chinese University of Hong Kong; Jing Qin, The Hong Kong Polytechnic University; Pheng-Ann Heng, The Chinese Univsersity of Hong Kong

Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation Jamshid Sourati*, Boston Children's Hospital; Ali Gholipour, Boston Children's Hospital; Jennifer Dy, Northeastern; Sila Kurugol, Boston Children's Hospital and Harvard Medical School; Simon Warfield, Harvard University

Contextual Additive Networks to Efficiently Boost 3D Image Segmentations. Zhenlin Xu*, UNC Chapel Hill; Zhengyang Shen, UNC; Marc Niethammer, UNC

Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration. Julian Krebs*, Inria; Tommaso Mansi, Siemens Healthineers; Boris Mailhé, Siemens Healthineers; Nicholas Ayache, INRIA; Herve Delingette, Inria

Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-Organizing Maps. Lee Reid*, CSIRO; Alex Pagnozzi, CSIRO

Focal Dice Loss and Image Dilation for Brain Tumor Segmentation. Pei Wang*, Hong Kong University of Science and Technology; Albert Chung, Hong Kong University of Science and Technology

Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning Roman Spilger*, University of Heidelberg; Thomas Wollmann, University of Heidelberg; Yu Qiang, University of Heidelberg; Andrea Imle, University of Heidelberg; Ji Young Lee, University of Heidelberg; Barbara Müller, University of Heidelberg; Oliver Fackler, University of Heidelberg; Ralf Bartenschlager, University of Heidelberg; Karl Rohr, DKFZ

3D Convolutional Neural Networks for Classification of Functional Connectomes Meenakshi Khosla*, Cornell University; Keith Jamison, Cornell University; Amy Kuceyeski, Cornell University; Mert Sabuncu, Cornell

Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks Liesbeth Vandewinckele, KU Leuven; David Robben, KU Leuven; Wouter Crijns, UZ Leuven; Frederik Maes*, KU Leuven

Unpaired Deep Cross-modality Synthesis with Fast Training Lei Xiang*, Shanghai Jiao Tong University; Yang Li, University of North Carolina at Chapel Hill; Weili Lin, UNC Chapel Hill; Qian Wang, Shanghai Jiao Tong University; Dinggang Shen, UNC

UOLO - automatic object detection and segmentation in biomedical images Teresa Araújo*, INESC-TEC; Guilherme Aresta, INESC TEC; Adrian Galdran, INESC TEC; Pedro Costa, INESC TEC; Ana Maria Mendonça, Faculdade de Engenharia da Universidade do Porto; Aurélio Campilho, Faculdade de Engenharia da Universidade do Porto

Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN Heran Yang*, Xi'an Jiaotong University; Jian Sun, Xi'an Jiaotong University; Aaron Carass, Johns Hopkins University, USA; Can Zhao, johns hopkins university; Junghoon Lee, The Johns Hopkins University School of Medicine; Zongben Xu, Xi'an Jiaotong University; Jerry Prince, JHU

Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy Firat Ozdemir*, ETH Zurich; Zixuan Peng, ETH Zurich; Christine Tanner, ETH-Zurich; Philipp Fürnstahl, University Hospital Balgrist; Orcun Goksel, ETH Zurich

Weakly Supervised Localisation for Fetal Ultrasound Images Nicolas Toussaint*, King's College London; Bishesh Khanal, King's College London; Matthew Sinclair, Imperial College London; Alberto Gomez, KCL; Emily Skelton, King's College London; Jacqueline Matthew, KCL; Julia Schnabel, King's College London

PIMMS: Permutation Invariant Multi-Modal Segmentation Thomas Varsavsky*, University College London; Zach Eaton-Rosen, UCL; Carole Sudre, UCL; Parashkev Nachev, University College London; M. Jorge Cardoso, UCL

Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and Wasserstein autoencoders: application to epilepsy detection zaruhi Alaverdyan*, CREATIS/INSA Lyon; Jiazheng Chai, CREATIS/INSA Lyon; Carole Lartizien, CREATIS

Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior Yechiel Lamash*, Boston Children's Hospital and Harvard Medical School; Sila Kurugol, Boston Children's Hospital and Harvard Medical School; Simon Warfield, Harvard University

Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images Aditya Sharma*, IIT MANDI; Prabhjot Kaur, Indian Institute of Technology, Mandi; Aditya Nigam, IIT mandi; Arnav Bhavsar, IIT Mandi

Nonlinear adaptively learned optimization for object localization in 3D medical images Mayalen Etcheverry*, Siemens Healthineers; Bogdan Georgescu, Siemens Healthineers; Benjamin Odry, Siemens Healthineers; Thomas Re, Siemens Healthineers; Shivam Kaushik, Siemens Healthineers; Bernhard Geiger, Siemens Healthineers; Mariappan Nadar, Siemens Healthineers; Sasa Grbic, Siemens Healthineers; Dorin Comaniciu, Siemens Healthineers

SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays Wei Dai*, Petuum Inc.; Nanqing Dong, Petuum Inc.; Zeya Wang, Petuum Inc.; Xiaodan Liang, Petuum Inc.; Hao Zhang, Petuum Inc.; Eric Xing, Petuum Inc.

Computed Tomography Image Enhancement using 3D Convolutional Neural Network MENG LI*, Peking University; Shiwen Shen, UCLA; Wen Gao, PKU; William Hsu, UCLA; Jason Cong, Nil

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks Vladimir Iglovikov, ods.ai; Alexander Rakhlin, Neuromation OU; Alexandr A. Kalinin*, University of Michigan; Alexey Shvets, Massachusetts Institute of Technology

Automatic myocardial strain imaging in echocardiography using deep learning Andreas Østvik*, Norwegian University of Science and Technology; Erik Smistad, Norwegian University of Science and Technology; Torvald Espeland, Norwegian University of Science and Technology; Erik Andreas Rye Berg, Norwegian University of Science and Technology; Lasse Løvstakken, Norwegian University of Science and Technology

Longitudinal detection of radiological abnormalities with time-modulated LSTM Giovanni Montana, Kings College London; Ruggiero Santeramo*, University of Warwick; Samuel Withey, King's College London

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease Danielle Pace*, MIT; Adrian Dalca, MIT; Tom Brosch, Philips Research Hamburg; Tal Geva, Boston Children's Hospital; Andrew Powell, Boston Children's Hospital; Jürgen Weese, Philips GmbH Innovative Technologies; Mehdi Hedjazi, Harvard Medical School and Boston Children's Hospital; Polina Golland, MIT

ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans Felix Lau*, Arterys; Tom Hendriks, University Medical Center Groningen; Jesse Lieman-Sifry, Arterys; Sean Sall, Arterys; Daniel Golden, Arterys

Oral presentations will have a chance to have a poster presentation:

Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network. Abdullah-Al-Zubaer Imran*, University of California, Los Angeles; Ali Hatamizadeh, University of California, Los Angeles; Shilpa Pundi Ananth, VoxelCloud Inc; Xiaowei Ding, VoxelCloud Inc.; Demetri Terzopoulos, University of California, Los Angeles; Nima Tajbakhsh, VoxelCloud Inc. 

Learning to Segment Medical Images with Scribble-Supervision Alone. Yigit Baran Can, ETH Zurich; Krishna Chaitanya, ETH Zurich; Basil Mustafa, University of Cambridge; Lisa Koch, ETH Zurich; Ender Konukoglu, ETH Zurich; Christian Baumgartner *, ETH Zurich. 

Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification. Veronica Vilaplana*, Technical University of Catalonia (UPC) 

Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images Nanqing Dong*, Cornell University; Michael C. Kampffmeyer, Universitet i Tromsø; Xiaodan Liang, Carnegie Mellon University; Zeya Wang, Rice University; Wei Dai, Petuum Inc.; Eric P. Xing, Carnegie Mellon University 

Multi-Scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification Liying Peng*, Zhejiang University, China; Lanfen Lin, Zhejiang University; Hongjie Hu, Department of Radiology, Sir Run Run Shaw Hospital; huali li, Department of Radiology, Sir Run Run Shaw Hospital; Qingqing Chen, Zhejiang University School of Medicine,Sir Run Run Shaw Hospital; dan wang, Department of Radiology, Sir Run Run Shaw Hospital; Xian-Hua Han, Yamaguchi University; Yutaro Iwamoto, Ritsumeikan University; Yen-Wei Chen, Ritsumeikan University 

UNet++: A Nested U-Net Architecture for Medical Image Segmentation Zongwei Zhou, Arizona State University; Md Mahfuzur Rahman Siddiquee, Arizona State University; Nima Tajbakhsh, Arizona State University; Jianming Liang*, Arizona State University, USA 

Learning Optimal Deep Projection of 18F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes Shubham Kumar *, Technical University of Munich; Abhijit Guha Roy, Ludwig Maximilian University; Ping Wu, Fudan University; Sailesh Conjeti, Technical University of Munich; R. S. Anand, Indian Institute of Technology, Roorkee; Jian Wang, Fudan University; Igor Yakushev, Technical University of Munich; Stefan Förster, Technical University of Munich; Markus Schwaiger, Klinikum Rechts der Isar, TUM; Sung-Cheng Huang, University of California, Los Angeles ; Axel Rominger, University of Munich; Chuantao Zuo, Fudan; Kuangyu Shi, Technical University of Munich shubhamkumar.iitr@gmail.com 

Industry Session

Imsight (3:40-3:55pm)

Hyperfine (3:55 - 4:10pm)

Nvidia (4:10-4:25pm)

Invited Talk 3 by Christopher Semturs (Google Research)

Deep Learning for Retinal Imaging