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  • Conference proceedings
  • © 2020

Interpretable and Annotation-Efficient Learning for Medical Image Computing

Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

Conference proceedings info: IMIMIC 2020, LABELS 2020, MIL3ID 2020.

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Table of contents (30 papers)

  1. Front Matter

    Pages i-xvii
  2. iMIMIC 2020

    1. Front Matter

      Pages 1-1
    2. Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers

      • Eren Bora Yilmaz, Alexander Oliver Mader, Tobias Fricke, Jaime Peña, Claus-Christian Glüer, Carsten Meyer
      Pages 3-12
    3. Projective Latent Interventions for Understanding and Fine-Tuning Classifiers

      • Andreas Hinterreiter, Marc Streit, Bernhard Kainz
      Pages 13-22
    4. Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging

      • Mara Graziani, Thomas Lompech, Henning Müller, Adrien Depeursinge, Vincent Andrearczyk
      Pages 23-32
    5. Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-Based Explanations

      • Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont, Saïd Ladjal, Isabelle Bloch
      Pages 43-53
    6. Explainable Disease Classification via Weakly-Supervised Segmentation

      • Aniket Joshi, Gaurav Mishra, Jayanthi Sivaswamy
      Pages 54-62
    7. Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns

      • Maximilian Möller, Matthias Kohl, Stefan Braunewell, Florian Kofler, Benedikt Wiestler, Jan S. Kirschke et al.
      Pages 63-72
    8. Explainability for Regression CNN in Fetal Head Circumference Estimation from Ultrasound Images

      • Jing Zhang, Caroline Petitjean, Florian Yger, Samia Ainouz
      Pages 73-82
  3. MIL3ID 2020

    1. Front Matter

      Pages 83-83
    2. Recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins

      • Özgün Çiçek, Yassine Marrakchi, Enoch Boasiako Antwi, Barbara Di Ventura, Thomas Brox
      Pages 85-93
    3. Semi-supervised Instance Segmentation with a Learned Shape Prior

      • Long Chen, Weiwen Zhang, Yuli Wu, Martin Strauch, Dorit Merhof
      Pages 94-102
    4. COMe-SEE: Cross-modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs

      • Angshuman Paul, Thomas C. Shen, Niranjan Balachandar, Yuxing Tang, Yifan Peng, Zhiyong Lu et al.
      Pages 103-111
    5. Semi-supervised Machine Learning with MixMatch and Equivalence Classes

      • Colin B. Hansen, Vishwesh Nath, Riqiang Gao, Camilo Bermudez, Yuankai Huo, Kim L. Sandler et al.
      Pages 112-121
    6. Non-contrast CT Liver Segmentation Using CycleGAN Data Augmentation from Contrast Enhanced CT

      • Chongchong Song, Baochun He, Hongyu Chen, Shuangfu Jia, Xiaoxia Chen, Fucang Jia
      Pages 122-129
    7. A Case Study of Transfer of Lesion-Knowledge

      • Soundarya Krishnan, Rishab Khincha, Lovekesh Vig, Tirtharaj Dash, Ashwin Srinivasan
      Pages 138-145
    8. Transfer Learning with Joint Optimization for Label-Efficient Medical Image Anomaly Detection

      • Xintong Li, Huijuan Yang, Zhiping Lin, Pavitra Krishnaswamy
      Pages 146-154
    9. Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-domain Liver Segmentation

      • Chenyu You, Junlin Yang, Julius Chapiro, James S. Duncan
      Pages 155-163

Other Volumes

  1. Interpretable and Annotation-Efficient Learning for Medical Image Computing

    Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

About this book

This book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020.

The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing.

Keywords

  • artificial intelligence
  • bioinformatics
  • classification
  • computer vision
  • deep learning
  • image analysis
  • image processing
  • image reconstruction
  • image segmentation
  • imaging systems
  • imperfect data
  • machine learning
  • medical image analysis
  • medical images
  • neural networks
  • pattern recognition

Editors and Affiliations

  • University of Porto, Porto, Portugal

    Jaime Cardoso, Wilson Silva, Ricardo Cruz

  • University of Houston, Houston, USA

    Hien Van Nguyen, Badri Roysam

  • University of Minnesota, Minneapolis, USA

    Nicholas Heller

  • University of Coimbra, Coimbra, Portugal

    Pedro Henriques Abreu, Jose Pereira Amorim

  • Amsterdam University Medical Center, Amsterdam, The Netherlands

    Ivana Isgum

  • Johns Hopkins University, Baltimore, USA

    Vishal Patel

  • Chinese Academy of Sciences, Beijing, China

    Kevin Zhou

  • UT Southwestern Medical Center, Dallas, USA

    Steve Jiang

  • University of Arkansas, Fayetteville, USA

    Ngan Le, Khoa Luu

  • University of Bern, Bern, Switzerland

    Raphael Sznitman

  • Eindhoven University of Technology, Eindhoven, The Netherlands

    Veronika Cheplygina, Samaneh Abbasi

  • Technical University of Munich, Nantes, Germany

    Diana Mateus

  • University of Dundee, Dundee, UK

    Emanuele Trucco

Bibliographic Information

  • Book Title: Interpretable and Annotation-Efficient Learning for Medical Image Computing

  • Book Subtitle: Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

  • Editors: Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo Cruz, Jose Pereira Amorim, Vishal Patel, Badri Roysam, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Samaneh Abbasi

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-61166-8

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Softcover ISBN: 978-3-030-61165-1

  • eBook ISBN: 978-3-030-61166-8

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVII, 292

  • Number of Illustrations: 109 b/w illustrations

  • Topics: Artificial Intelligence, Computer Vision, Computer Application in Social and Behavioral Sciences, Computational and Systems Biology, Automated Pattern Recognition

Buying options

eBook USD 64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-61166-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 84.99
Price excludes VAT (USA)