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Data Augmentation, Labelling, and Imperfections

Third MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Yuan Xue

    1. Ohio State University, Columbus, USA

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  2. Chen Chen

    1. University of Sheffield, Sheffield, UK

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  3. Chao Chen

    1. Stony Brook University, Stony Brook, USA

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  4. Lianrui Zuo

    1. Johns Hopkins University, Baltimore, USA

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  5. Yihao Liu

    1. Johns Hopkins University, Baltimore, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14379)

Included in the following conference series:

Conference proceedings info: MICCAI 2023.

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About this book

This LNCS conference volume constitutes the proceedings of the 3rd International Workshop on

Data Augmentation, Labeling, and Imperfections (DALI 2023), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th International

Conference on Medical Image Computing and Computer Assisted Intervention

(MICCAI 2023). The 16 full papers together in this volume were carefully reviewed and selected from 23 submissions.

The conference fosters a collaborative environment for addressing the critical challenges associated with medical data, particularly focusing on data, labeling, and dealing with data imperfections in the context of medical image analysis.

Keywords

Table of contents (16 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. URL: Combating Label Noise for Lung Nodule Malignancy Grading

    • Xianze Ai, Zehui Liao, Yong Xia
    Pages 1-11

  3. Zero-Shot Learning of Individualized Task Contrast Prediction from Resting-State Functional Connectomes

    • Minh Nguyen, Gia H. Ngo, Mert R. Sabuncu
    Pages 12-22

  4. Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis

    • Shijie Li, Mengwei Ren, Thomas Ach, Guido Gerig
    Pages 23-32

  5. A Unified Approach to Learning with Label Noise and Unsupervised Confidence Approximation

    • Navid Rabbani, Adrien Bartoli
    Pages 33-42

  6. Transesophageal Echocardiography Generation Using Anatomical Models

    • Emmanuel Oladokun, Musa Abdulkareem, Jurica Šprem, Vicente Grau
    Pages 43-52

  7. Data Augmentation Based on DiscrimDiff for Histopathology Image Classification

    • Xianchao Guan, Yifeng Wang, Yiyang Lin, Yongbing Zhang
    Pages 53-62

  8. Clinically Focussed Evaluation of Anomaly Detection and Localisation Methods Using Inpatient CT Head Data

    • Antanas Kascenas, Chaoyang Wang, Patrick Schrempf, Ryan Grech, Hui Lu Goh, Mark Hall et al.
    Pages 63-72

  9. LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation

    • Berke Doga Basaran, Weitong Zhang, Mengyun Qiao, Bernhard Kainz, Paul M. Matthews, Wenjia Bai
    Pages 73-83

  10. Knowledge Graph Embeddings for Multi-lingual Structured Representations of Radiology Reports

    • Tom van Sonsbeek, Xiantong Zhen, Marcel Worring
    Pages 84-94

  11. Modular, Label-Efficient Dataset Generation for Instrument Detection for Robotic Scrub Nurses

    • Jorge Badilla-Solórzano, Nils-Claudius Gellrich, Thomas Seel, Sontje Ihler
    Pages 95-105

  12. Adaptive Semi-supervised Segmentation of Brain Vessels with Ambiguous Labels

    • Fengming Lin, Yan Xia, Nishant Ravikumar, Qiongyao Liu, Michael MacRaild, Alejandro F. Frangi
    Pages 106-116

  13. Proportion Estimation by Masked Learning from Label Proportion

    • Takumi Okuo, Kazuya Nishimura, Hiroaki Ito, Kazuhiro Terada, Akihiko Yoshizawa, Ryoma Bise
    Pages 117-126

  14. Active Learning Strategies on a Real-World Thyroid Ultrasound Dataset

    • Hari Sreedhar, Guillaume P. R. Lajoinie, Charles Raffaelli, Hervé Delingette
    Pages 127-136

  15. A Realistic Collimated X-Ray Image Simulation Pipeline

    • Benjamin El-Zein, Dominik Eckert, Thomas Weber, Maximilian Rohleder, Ludwig Ritschl, Steffen Kappler et al.
    Pages 137-145

  16. Masked Conditional Diffusion Models for Image Analysis with Application to Radiographic Diagnosis of Infant Abuse

    • Shaoju Wu, Sila Kurugol, Andy Tsai
    Pages 146-156

  17. Self-supervised Single-Image Deconvolution with Siamese Neural Networks

    • Mikhail Papkov, Kaupo Palo, Leopold Parts
    Pages 157-166

  18. Back Matter

    Pages 167-168
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  7. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  8. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  9. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  10. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

  11. Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology

  12. Data Augmentation, Labelling, and Imperfections

  13. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops

  14. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops

  15. Deep Generative Models

  16. Myopic Maculopathy Analysis

Editors and Affiliations

  • Ohio State University, Columbus, USA

    Yuan Xue

  • University of Sheffield, Sheffield, UK

    Chen Chen

  • Stony Brook University, Stony Brook, USA

    Chao Chen

  • Johns Hopkins University, Baltimore, USA

    Lianrui Zuo, Yihao Liu

Bibliographic Information

  • Book Title: Data Augmentation, Labelling, and Imperfections

  • Book Subtitle: Third MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings

  • Editors: Yuan Xue, Chen Chen, Chao Chen, Lianrui Zuo, Yihao Liu

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-58171-7

  • 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 2024

  • Softcover ISBN: 978-3-031-58170-0Due: 28 May 2024

  • eBook ISBN: 978-3-031-58171-7Published: 26 April 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 168

  • Number of Illustrations: 5 b/w illustrations, 50 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence, Computing Milieux

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