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Medical Image Learning with Limited and Noisy Data

First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Ghada Zamzmi

    1. National Institutes of Health, Bethesda, USA

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  2. Sameer Antani

    1. National Institutes of Health, Bethesda, USA

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  3. Ulas Bagci

    1. Northwestern University, Chicago, USA

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  4. Marius George Linguraru

    1. Children's National Hospital, Washington, USA

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  5. Sivaramakrishnan Rajaraman

    1. National Institutes of Health, Bethesda, USA

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  6. Zhiyun Xue

    1. National Institutes of Health, Bethesda, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13559)

Included in the following conference series:

Conference proceedings info: MILLanD 2022.

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Table of contents (22 papers)

  1. Front Matter

    Pages i-xi
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  2. Efficient and Robust Annotation Strategies

    1. Front Matter

      Pages 1-1
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    2. Heatmap Regression for Lesion Detection Using Pointwise Annotations

      • Chelsea Myers-Colet, Julien Schroeter, Douglas L. Arnold, Tal Arbel
      Pages 3-12

    3. Partial Annotations for the Segmentation of Large Structures with Low Annotation Cost

      • Bella Specktor Fadida, Daphna Link Sourani, Liat Ben Sira, Elka Miller, Dafna Ben Bashat, Leo Joskowicz
      Pages 13-22

    4. Abstraction in Pixel-wise Noisy Annotations Can Guide Attention to Improve Prostate Cancer Grade Assessment

      • Hyeongsub Kim, Seo Taek Kong, Hongseok Lee, Kyungdoc Kim, Kyu-Hwan Jung
      Pages 23-31

    5. Meta Pixel Loss Correction for Medical Image Segmentation with Noisy Labels

      • Zhuotong Cai, Jingmin Xin, Peiwen Shi, Sanping Zhou, Jiayi Wu, Nanning Zheng
      Pages 32-41

    6. Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction

      • Hanxiao Zhang, Xiao Gu, Minghui Zhang, Weihao Yu, Liang Chen, Zhexin Wang et al.
      Pages 42-51

  3. Weakly-Supervised, Self-supervised, and Contrastive Learning

    1. Front Matter

      Pages 53-53
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    2. Universal Lesion Detection and Classification Using Limited Data and Weakly-Supervised Self-training

      • Varun Naga, Tejas Sudharshan Mathai, Angshuman Paul, Ronald M. Summers
      Pages 55-64

    3. BoxShrink: From Bounding Boxes to Segmentation Masks

      • Michael Gröger, Vadim Borisov, Gjergji Kasneci
      Pages 65-75

    4. Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis

      • Xiao Qi, David J. Foran, John L. Nosher, Ilker Hacihaliloglu
      Pages 76-85

    5. SB-SSL: Slice-Based Self-supervised Transformers for Knee Abnormality Classification from MRI

      • Sara Atito, Syed Muhammad Anwar, Muhammad Awais, Josef Kittler
      Pages 86-95

    6. Optimizing Transformations for Contrastive Learning in a Differentiable Framework

      • Camille Ruppli, Pietro Gori, Roberto Ardon, Isabelle Bloch
      Pages 96-105

    7. Stain Based Contrastive Co-training for Histopathological Image Analysis

      • Bodong Zhang, Beatrice Knudsen, Deepika Sirohi, Alessandro Ferrero, Tolga Tasdizen
      Pages 106-116

  4. Active and Continual Learning

    1. Front Matter

      Pages 117-117
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    2. CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification

      • Suraj Kothawade, Atharv Savarkar, Venkat Iyer, Ganesh Ramakrishnan, Rishabh Iyer
      Pages 119-129

    3. Real Time Data Augmentation Using Fractional Linear Transformations in Continual Learning

      • Arijit Patra
      Pages 130-140

    4. DIAGNOSE: Avoiding Out-of-Distribution Data Using Submodular Information Measures

      • Suraj Kothawade, Akshit Shrivastava, Venkat Iyer, Ganesh Ramakrishnan, Rishabh Iyer
      Pages 141-150

  5. Transfer Representation Learning

    1. Front Matter

      Pages 151-151
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    2. Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning

      • Peidi Xu, Faezeh Moshfeghifar, Torkan Gholamalizadeh, Michael Bachmann Nielsen, Kenny Erleben, Sune Darkner
      Pages 153-162
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  1. Medical Image Learning with Limited and Noisy Data

Keywords

About this book

This book constitutes the proceedings of the First Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with MICCAI 2022. The conference was held in Singapore. For this workshop, 22 papers from 54 submissions were accepted for publication. They selected papers focus on the 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.

Editors and Affiliations

  • National Institutes of Health, Bethesda, USA

    Ghada Zamzmi, Sameer Antani, Sivaramakrishnan Rajaraman, Zhiyun Xue

  • Northwestern University, Chicago, USA

    Ulas Bagci

  • Children's National Hospital, Washington, USA

    Marius George Linguraru

Bibliographic Information

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

  • Book Subtitle: First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

  • Editors: Ghada Zamzmi, Sameer Antani, Ulas Bagci, Marius George Linguraru, Sivaramakrishnan Rajaraman, Zhiyun Xue

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-16760-7

  • 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 2022

  • Softcover ISBN: 978-3-031-16759-1Published: 22 September 2022

  • eBook ISBN: 978-3-031-16760-7Published: 21 September 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 240

  • Number of Illustrations: 6 b/w illustrations, 71 illustrations in colour

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

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