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The workshop DLMIA has become one of the most successful MICCAI satellite events, with hundreds of attendees and more than 70 paper submissions in 2017 (please check DLMIA 2017 page). The 4th edition of DLMIA will be dedicated to the presentation of papers focused on the design and use of deep learning methods for medical image and data analysis applications. We believe that this workshop is setting the trends and identifying the challenges of the use of deep learning methods in medical image and data analysis. Another important objective of the workshop is to continue and increase the connection between software developers, researchers and end-users from diverse fields related to medical image and signal processing, which are the main scopes of MICCAI. 

The proceedings will published by SPRINGER under the “Lecture Notes in Computer Science” book series.

**** FREE ACCESS TO THE PROCEEDINGS UNTIL OCTOBER 19th, 2018 here ***

Papers should be submitted electronically (LNCS style, double blind review - submissions are to be fully anonymized) of up to 8-page papers for oral or poster presentation in the CMT system (submissions exceeding this limit will be rejected without review - the CMT submission system will be available soon).  Latex style files can be found on the Springer website, which also contains Office Word instructions (scroll to the bottom half of the web page). The file format for submissions is Adobe Portable Document Format (PDF). Other formats will not be accepted. Please check the PDF document properties tab to confirm that the author field does not break anonymity. It is also possible to submit supplementary material with your submission. The deadline for submitting the supplementary material is the same as for the main paper.  Submissions should describe original work that is not under peer review or has been accepted for a publication elsewhere (including conference and journal) before the DLMIA workshop in September 2018. Authors are allowed to submit a novel research manuscript that has been archived for future dissemination (e.g. on the arXiv platform).

To submit your paper, please click here

Topic of Interest:
    ● Medical imaging-based analysis using deep learning
    ● Medical signal-based analysis using deep learning
    ● Medical image reconstruction using deep learning
    ● Deep learning-oriented applications in medicine
    ● Image description and synthesis using deep learning techniques
    ● Deep learning model selection in medical imaging
    ● Multi-modal and multi-dimensional deep learning (3D, 4D, and beyond)
    ● Learning with noisy labels ( eg. crowdsourcing annotations, imperfect ground truth etc.)
    ● Integration of clinical variables with imaging data
    ● Deep learning for interventional image analysis
    ● Benchmarking and Evaluation of deep learning in clinical settings
    ● Active Deep Learning for medical imaging
    ● Reinforcement learning and Meta-learning in Medical Image Analysis
Important Dates
● Full Paper Submission: Extended to June 15th 2018 (11:59pm PST)
● Notification of Acceptance: July 11th 2018
● Camera-Ready Version: July 17th 2018
● Conference Date: September 20th, 2018


Keynote Speakers

Prof. Hayit Greenspan

Prof. Hayit Greenspan

Tel Aviv University

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

Prof. Alison Noble

Prof. Alison Noble

University of Oxford

Prof. Alison Noble

Mr. Christopher Semturs

 Google Research

Deep Learning for Retinal Imaging

Preliminary Program

9:45am-10:00am

Opening remarks

10:00am-11:00am

Invited Talk by Prof. Hayit Greenspan

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

11:00am-11:30am

Coffee Break

11:30am-12:30pm

Invited Talk by Prof. Alison Noble

12:30pm-1:30pm

Oral presentations - Session 1

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. (12:30-12:50pm)

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. (12:50-1:10pm)

Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification. Marc Combalia, Veronica Vilaplana*, Technical University of Catalonia (UPC) (1:10-1:30pm)

1:30-3:00pm

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 

3:00-3:40pm

Oral presentations - Session 2

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 (3:00-3:20pm)

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 (3:20-3:40pm)

3:40-4:40pm

Industry Session

Imsight (3:40-3:55pm)

Hyperfine (3:55 - 4:10pm)

Nvidia (4:10-4:25pm)

4:25-5:00pm

Coffee Break

5:00-6:00pm

Invited Talk 3 by Christopher Semturs (Google Research)

Deep Learning for Retinal Imaging

6:00-6:40pm

Oral presentations - Session 3

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 (6:00-6:20pm)

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 (6:20-6:40pm)

6:40-7:00pm

Awards Ceremony and closing remarks

Organisers

Dr. Gustavo Carneiro

The University of Adelaide
Australia

Dr. João Manuel R. S. Tavares

Universidade do Porto
Portugal

Dr. Andrew P. Bradley

Queensland University of Technology, Australia

Dr. João Paulo Papa

Universidade Estadual Paulista
Brazil

Dr. Vasileios Belagiannis

Osram
Germany

Dr. Jacinto C. Nascimento

Instituto Superior Tecnico
Portugal

Dr. Zhi Lu

Guangdong University of Technology
China

Program Chair

Dr. Sailesh Conjeti

German Center for Neurodegenerative Diseases
Germany

Program Committee

Name Affiliation
Aaron Carass
Abhijit Guha Roy
Adrian Barbu 
Adrian Johnston
Adrien Depeursinge
Amarjot Singh
Amir Jamaludin
Anees Kazi
Anil Anthony Bharath 
Anjany Sekuboyina
Ankush Gupta
Ariel Benou
Avi Ben-Cohen
Azade Farshad
Carlos Santiago 
Carlos Arteta
Carole Sudre
Catarina Barata 
Chia-Yu Kao
Christoph Baur
Dario Augusto B. Oliveira 
David Ribeiro
Diogo Pernes
Dong Guo 
Dorit Merhof
Eduardo Soudah
Erik Smistad 
Eugene Vorontsov
Fernando Navarro
Gabriel Maicas 
Ghassan Hamarneh
Gregory Slabaugh
Helder Oliveira 
Holger Roth
Jianming Liang
Kelwin Fernandes,
Kyu-Hwan Jung
Lisa Tang 
Magdalini Paschali
Mans Larsson
Manuel Marques 
Maria Gabrani
Marleen de Bruijne
Mehmet Aygun 
Nico Hoffmann
Nishikant Deshmukh
Pew-Thian Yap 
Pheng-Ann Heng
Prasad Sudhakar
Rahul Venkataramani 
Roger Tam
Saad Ullah Akram
Simon Pezold 
Steffen Schneider
Takayuki Kitasaka
Toan Tran 
Tom Brosch
Weidong Cai
Xiang Xiang 
Xiaodong Wu
Yong Xia
Youngjin Yoo 
Yungeng Zhang
Zhi Huang
Ziyue Xu.

Johns Hopkins University, USA
Indian Institute of Technology Kharagpur, India
Florida State University, USA
University of Adelaide
HES-SO and EPFL, Switzerland
University of Cambridge
University of Oxford
Technical University of Munich
Imperial College of London,UK
TUM
University of Oxford
Ben-Gurion University of the Negev
Tel Aviv University
TUM
Instituto Superior Tecnico
Oxford
UCL
Instituto Superior Técnico
University of North Carolina at Chapel Hill
Technische Universität München, Germany
IBM Research
Instituto Superior Técnico
INESC TEC
University of Southern California
RWTH Aachen University
International Center for Numerical Methods in Engineering, Spain
Norwegian University of Science and Technology
Polytechnique Montreal
Technical University of Munich
University of Adelaide
Simon Fraser University
Huawei Research and Development
Universidade do Porto
NVIDIA
Arizona State University, USA
Universidade do Porto
VUNO Inc.
UBC
Technische Universität München
Chalmers
Instituto Superior Tecnico, Portugal
IBM Research Zurich
Erasmus MC Rotterdam / University of Copenhagen
Istanbul Technical University
TU Dresden
Johns Hopkins University
UNC-Chapel Hill
The Chinese Univsersity of Hong Kong
GE Global Research
GE Global Research
The University of British Columbia, Canada
University of Oulu, Finland
MIAC, University of Basel, CH
University of Tübingen
Aichi Institute of Technology, Japan
University of Adelaide
The University of British Columbia, Canada
University of Sydney
Johns Hopkins University
University of Iowa
Northwestern Polytechnical University
University of British Columbia
Peking University
Purdue University
National Institutes of Health

OUR AWESOME SPONSORS

Main Contact

Dr. Gustavo Carneiro
gustavo.carneiro@adelaide.edu.au