Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings

  • Qian Wang
  • Fausto Milletari
  • Hien V. Nguyen
  • Shadi Albarqouni
  • M. Jorge Cardoso
  • Nicola Rieke
  • Ziyue Xu
  • Konstantinos Kamnitsas
  • Vishal Patel
  • Badri Roysam
  • Steve Jiang
  • Kevin Zhou
  • Khoa Luu
  • Ngan Le
Conference proceedings DART 2019, MIL3ID 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11795)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. DART 2019

    1. Front Matter
      Pages 1-1
    2. Ilja Manakov, Markus Rohm, Christoph Kern, Benedikt Schworm, Karsten Kortuem, Volker Tresp
      Pages 3-10
    3. Gabriele Valvano, Agisilaos Chartsias, Andrea Leo, Sotirios A. Tsaftaris
      Pages 11-19
    4. Ozan Ciga, Jianan Chen, Anne Martel
      Pages 20-27
    5. Zahil Shanis, Samuel Gerber, Mingchen Gao, Andinet Enquobahrie
      Pages 28-36
    6. Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni
      Pages 37-44
    7. Eric Kerfoot, Esther Puyol-Antón, Bram Ruijsink, Rina Ariga, Ernesto Zacur, Pablo Lamata et al.
      Pages 45-53
    8. Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen et al.
      Pages 54-62
    9. Yucheng Liu, Naji Khosravan, Yulin Liu, Joseph Stember, Jonathan Shoag, Ulas Bagci et al.
      Pages 63-71
    10. Feng Zhang, Yutong Xie, Yong Xia, Yanning Zhang
      Pages 72-80
    11. Yilin Liu, Gregory R. Kirk, Brendon M. Nacewicz, Martin A. Styner, Mingren Shen, Dong Nie et al.
      Pages 81-89
    12. Barleen Kaur, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas Arnold et al.
      Pages 90-98
  3. MIL3ID 2019

    1. Front Matter
      Pages 109-109
    2. Ortal Senouf, Sanketh Vedula, Tomer Weiss, Alex Bronstein, Oleg Michailovich, Michael Zibulevsky
      Pages 111-119
    3. Shiqi Peng, Bolin Lai, Guangyu Yao, Xiaoyun Zhang, Ya Zhang, Yan-Feng Wang et al.
      Pages 120-128
    4. Xiao Chen, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han et al.
      Pages 129-138
    5. Bo Zhou, Adam P. Harrison, Jiawen Yao, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao et al.
      Pages 139-147
    6. Dhruv Sharma, Zahil Shanis, Chandan K. Reddy, Samuel Gerber, Andinet Enquobahrie
      Pages 148-156
    7. Jeremy Tan, Anselm Au, Qingjie Meng, Bernhard Kainz
      Pages 157-164
    8. Karin van Garderen, Marion Smits, Stefan Klein
      Pages 165-172
    9. Yunguan Fu, Maria R. Robu, Bongjin Koo, Crispin Schneider, Stijn van Laarhoven, Danail Stoyanov et al.
      Pages 173-180
    10. Chengliang Dai, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai
      Pages 199-206
    11. Zahra Mirikharaji, Yiqi Yan, Ghassan Hamarneh
      Pages 207-215
    12. Fidel A. Guerrero-Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Alexandre Cunha
      Pages 216-224
    13. Khalil Ouardini, Huijuan Yang, Balagopal Unnikrishnan, Manon Romain, Camille Garcin, Houssam Zenati et al.
      Pages 225-234
    14. Mina Amiri, Rupert Brooks, Hassan Rivaz
      Pages 235-242
    15. Toan Duc Bui, Li Wang, Jian Chen, Weili Lin, Gang Li, Dinggang Shen
      Pages 243-251
  4. Back Matter
    Pages 253-254

About these proceedings


This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains.

MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection. 


artificial intelligence ct image image analysis image reconstruction image segmentation imaging systems learning algorithms machine learning medical images medical imaging neural networks segmentation methods semi-supervised learning supervised learning

Editors and affiliations

  • Qian Wang
    • 1
  • Fausto Milletari
    • 2
  • Hien V. Nguyen
    • 3
  • Shadi Albarqouni
    • 4
  • M. Jorge Cardoso
    • 5
  • Nicola Rieke
    • 6
  • Ziyue Xu
    • 7
  • Konstantinos Kamnitsas
    • 8
  • Vishal Patel
    • 9
  • Badri Roysam
    • 10
  • Steve Jiang
    • 11
  • Kevin Zhou
    • 12
  • Khoa Luu
    • 13
  • Ngan Le
    • 14
  1. 1.Shanghai Jiaotong UniversityShanghaiChina
  2. 2.NVIDIA GmbHMunichGermany
  3. 3.University of HoustonHoustonUSA
  4. 4.Technical University MunichMunichGermany
  5. 5.King's College LondonLondonUK
  6. 6.NVIDIA GmbHMunichGermany
  7. 7.NVIDIASanta ClaraUSA
  8. 8.Imperial College LondonLondonUK
  9. 9.Johns Hopkins UniversityBaltimoreUSA
  10. 10.University of HoustonHoustonUSA
  11. 11.UT Southwestern Medical CenterDallasUSA
  12. 12.Chinese Academy of SciencesBeijingChina
  13. 13.University of ArkansasFayettevilleUSA
  14. 14.University of ArkansasFayettevilleUSA

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