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Medical Image Learning with Limited and Noisy Data

Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Zhiyun Xue

    1. National Library of Medicine, National Institutes of Health, Bethesda, USA

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  2. Sameer Antani

    1. National Library of Medicine, National Institutes of Health, Bethesda, USA

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  3. Ghada Zamzmi

    1. National Library of Medicine, National Institutes of Health, Bethesda, USA

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  4. Feng Yang

    1. National Library of Medicine, National Institutes of Health, Bethesda, USA

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  5. Sivaramakrishnan Rajaraman

    1. National Library of Medicine, National Institutes of Health, Bethesda, USA

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  6. Sharon Xiaolei Huang

    1. College of Information Sciences and Technology, Penn State University, University Park, USA

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  7. Marius George Linguraru

    1. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, USA

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  8. Zhaohui Liang

    1. National Library of Medicine, National Institutes of Health, Bethesda, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14307)

Included in the following conference series:

Conference proceedings info: MILLanD 2023.

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About this book

This book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023).

The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.


Keywords

Table of contents (24 papers)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Efficient Annotation and Training Strategies

    1. Front Matter

      Pages 1-1
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    2. Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations

      • Şerban Vădineanu, Daniël M. Pelt, Oleh Dzyubachyk, K. Joost Batenburg
      Pages 3-13

    3. ScribSD: Scribble-Supervised Fetal MRI Segmentation Based on Simultaneous Feature and Prediction Self-distillation

      • Yijie Qu, Qianfei Zhao, Linda Wei, Tao Lu, Shaoting Zhang, Guotai Wang
      Pages 14-23

    4. Label-Efficient Contrastive Learning-Based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images

      • Nazanin Moradinasab, Rebecca A. Deaton, Laura S. Shankman, Gary K. Owens, Donald E. Brown
      Pages 24-34

    5. Affordable Graph Neural Network Framework Using Topological Graph Contraction

      • Christopher Adnel, Islem Rekik
      Pages 35-46

  3. Approaches for Noisy, Missing, and Low Quality Data

    1. Front Matter

      Pages 47-47
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    2. Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Reconstruction of Low-Dose Cardiac SPECT

      • Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo, Qiong Liu, Albert J. Sinusas et al.
      Pages 49-59

    3. A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging

      • Yang Yu, Jiahao Wang, Ashish Jith Sreejith Kumar, Bryan Tan, Navya Vanjavaka, Nurul Hafidzah Rahim et al.
      Pages 60-70

    4. COVID-19 Lesion Segmentation Framework for the Contrast-Enhanced CT in the Absence of Contrast-Enhanced CT Annotations

      • Maryna Kvasnytsia, Abel Díaz Berenguer, Hichem Sahli, Jef Vandemeulebroucke
      Pages 71-81

    5. Feasibility of Universal Anomaly Detection Without Knowing the Abnormality in Medical Images

      • Can Cui, Yaohong Wang, Shunxing Bao, Yucheng Tang, Ruining Deng, Lucas W. Remedios et al.
      Pages 82-92

  4. Unsupervised, Self-supervised, and Contrastive Learning

    1. Front Matter

      Pages 93-93
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    2. Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection

      • Camille Ruppli, Pietro Gori, Roberto Ardon, Isabelle Bloch
      Pages 95-105

    3. FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation

      • Yunsung Chung, Chanho Lim, Chao Huang, Nassir Marrouche, Jihun Hamm
      Pages 106-116

    4. Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy

      • Tony Xu, Matthew Rozak, Edward Ntiri, Adrienne Dorr, James Mester, Bojana Stefanovic et al.
      Pages 117-127

    5. Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning

      • Zhaohui Liang, Zhiyun Xue, Sivaramakrishnan Rajaraman, Yang Feng, Sameer Antani
      Pages 128-137

    6. SDLFormer: A Sparse and Dense Locality-Enhanced Transformer for Accelerated MR Image Reconstruction

      • Rahul G.S., Sriprabha Ramnarayanan, Mohammad Al Fahim, Keerthi Ram, Preejith S.P, Mohanasankar Sivaprakasam
      Pages 138-147

    7. Robust Unsupervised Image to Template Registration Without Image Similarity Loss

      • Slim Hachicha, Célia Le, Valentine Wargnier-Dauchelle, Michaël Sdika
      Pages 148-157

    8. A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue

      • Jianfei Liu, Omid Shafaat, Ronald M. Summers
      Pages 158-167

  5. Weakly-Supervised, Semi-supervised, and Multitask Learning

    1. Front Matter

      Pages 169-169
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Other volumes

  1. Medical Image Learning with Limited and Noisy Data

Editors and Affiliations

  • National Library of Medicine, National Institutes of Health, Bethesda, USA

    Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Zhaohui Liang

  • College of Information Sciences and Technology, Penn State University, University Park, USA

    Sharon Xiaolei Huang

  • Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, USA

    Marius George Linguraru

Bibliographic Information

  • Book Title: Medical Image Learning with Limited and Noisy Data

  • Book Subtitle: Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

  • Editors: Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, … Zhaohui Liang

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-44917-8

  • Publisher: Springer Cham

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

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • Softcover ISBN: 978-3-031-47196-4Published: 31 October 2023

  • eBook ISBN: 978-3-031-44917-8Published: 07 October 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 270

  • Number of Illustrations: 5 b/w illustrations, 72 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics

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