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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

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Qian Wang

    1. Shanghai Jiaotong University, Shanghai, China

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  2. Fausto Milletari

    1. NVIDIA GmbH, Munich, Germany

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  3. Hien V. Nguyen

    1. University of Houston, Houston, USA

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  4. Shadi Albarqouni

    1. Technical University Munich, Munich, Germany

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  5. M. Jorge Cardoso

    1. King's College London, London, UK

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  6. Nicola Rieke

    1. NVIDIA GmbH, Munich, Germany

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  7. Ziyue Xu

    1. NVIDIA, Santa Clara, USA

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  8. Konstantinos Kamnitsas

    1. Imperial College London, London, UK

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  9. Vishal Patel

    1. Johns Hopkins University, Baltimore, USA

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  10. Badri Roysam

    1. University of Houston, Houston, USA

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  11. Steve Jiang

    1. UT Southwestern Medical Center, Dallas, USA

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  12. Kevin Zhou

    1. Chinese Academy of Sciences, Beijing, China

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  13. Khoa Luu

    1. University of Arkansas, Fayetteville, USA

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  14. Ngan Le

    1. University of Arkansas, Fayetteville, USA

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

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

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Table of contents (28 papers)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. DART 2019

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation

      • Ilja Manakov, Markus Rohm, Christoph Kern, Benedikt Schworm, Karsten Kortuem, Volker Tresp
      Pages 3-10

    3. Temporal Consistency Objectives Regularize the Learning of Disentangled Representations

      • Gabriele Valvano, Agisilaos Chartsias, Andrea Leo, Sotirios A. Tsaftaris
      Pages 11-19

    4. Multi-layer Domain Adaptation for Deep Convolutional Networks

      • Ozan Ciga, Jianan Chen, Anne Martel
      Pages 20-27

    5. Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training

      • Zahil Shanis, Samuel Gerber, Mingchen Gao, Andinet Enquobahrie
      Pages 28-36

    6. Learning Interpretable Disentangled Representations Using Adversarial VAEs

      • Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni
      Pages 37-44

    7. Synthesising Images and Labels Between MR Sequence Types with CycleGAN

      • Eric Kerfoot, Esther Puyol-Antón, Bram Ruijsink, Rina Ariga, Ernesto Zacur, Pablo Lamata et al.
      Pages 45-53

    8. Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning

      • Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen et al.
      Pages 54-62

    9. Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans

      • Yucheng Liu, Naji Khosravan, Yulin Liu, Joseph Stember, Jonathan Shoag, Ulas Bagci et al.
      Pages 63-71

    10. A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection

      • Feng Zhang, Yutong Xie, Yong Xia, Yanning Zhang
      Pages 72-80

    11. Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images

      • Yilin Liu, Gregory R. Kirk, Brendon M. Nacewicz, Martin A. Styner, Mingren Shen, Dong Nie et al.
      Pages 81-89

    12. Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

      • Barleen Kaur, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas Arnold et al.
      Pages 90-98

    13. Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performance of Endoscopic Lesions

      • Akihiro Fukuda, Tadashi Miyamoto, Shunsuke Kamba, Kazuki Sumiyama
      Pages 99-107

  3. MIL3ID 2019

    1. Front Matter

      Pages 109-109
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    2. Self-supervised Learning of Inverse Problem Solvers in Medical Imaging

      • Ortal Senouf, Sanketh Vedula, Tomer Weiss, Alex Bronstein, Oleg Michailovich, Michael Zibulevsky
      Pages 111-119

    3. Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-Propagation

      • Shiqi Peng, Bolin Lai, Guangyu Yao, Xiaoyun Zhang, Ya Zhang, Yan-Feng Wang et al.
      Pages 120-128

    4. A Cascade Attention Network for Liver Lesion Classification in Weakly-Labeled Multi-phase CT Images

      • Xiao Chen, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han et al.
      Pages 129-138

    5. CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT

      • Bo Zhou, Adam P. Harrison, Jiawen Yao, Chi-Tung Cheng, Jing Xiao, Chien-Hung Liao et al.
      Pages 139-147

    6. Active Learning Technique for Multimodal Brain Tumor Segmentation Using Limited Labeled Images

      • Dhruv Sharma, Zahil Shanis, Chandan K. Reddy, Samuel Gerber, Andinet Enquobahrie
      Pages 148-156
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  1. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Keywords

About this book

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. 

Editors and Affiliations

  • Shanghai Jiaotong University, Shanghai, China

    Qian Wang

  • NVIDIA GmbH, Munich, Germany

    Fausto Milletari, Nicola Rieke

  • University of Houston, Houston, USA

    Hien V. Nguyen, Badri Roysam

  • Technical University Munich, Munich, Germany

    Shadi Albarqouni

  • King's College London, London, UK

    M. Jorge Cardoso

  • NVIDIA, Santa Clara, USA

    Ziyue Xu

  • Imperial College London, London, UK

    Konstantinos Kamnitsas

  • Johns Hopkins University, Baltimore, USA

    Vishal Patel

  • UT Southwestern Medical Center, Dallas, USA

    Steve Jiang

  • Chinese Academy of Sciences, Beijing, China

    Kevin Zhou

  • University of Arkansas, Fayetteville, USA

    Khoa Luu, Ngan Le

Bibliographic Information

  • Book Title: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

  • Book Subtitle: 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

  • Editors: 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

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-33391-1

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-33390-4Published: 12 October 2019

  • eBook ISBN: 978-3-030-33391-1Published: 13 October 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVII, 254

  • Number of Illustrations: 34 b/w illustrations, 79 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Health Informatics

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