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Machine Learning for Multimodal Healthcare Data

First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. Andreas K. Maier

    1. Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

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  2. Julia A. Schnabel

    1. Technical University of Munich, Garching bei München, Germany

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  3. Pallavi Tiwari

    1. College of Engineering, University of Wisconsin–Madison, Madison, USA

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  4. Oliver Stegle

    1. German Cancer Research Center, Heidelberg, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14315)

Included in the following conference series:

Conference proceedings info: ML4MHD 2023.

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Table of contents (13 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Death Prediction by Race in Colorectal Cancer Patients Using Machine Learning Approaches

    • Frances M. Aponte-Caraballo, Frances Heredia-Negrón, Brenda G. Nieves-Rodriguez, Abiel Roche-Lima
    Pages 1-6

  3. Neural Graph Revealers

    • Harsh Shrivastava, Urszula Chajewska
    Pages 7-25

  4. Multi-modal Biomarker Extraction Framework for Therapy Monitoring of Social Anxiety and Depression Using Audio and Video

    • Tobias Weise, Paula Andrea Pérez-Toro, Andrea Deitermann, Bettina Hoffmann, Kubilay can Demir, Theresa Straetz et al.
    Pages 26-42

  5. RobustSsF: Robust Missing Modality Brain Tumor Segmentation with Self-supervised Learning-Based Scenario-Specific Fusion

    • Jeongwon Lee, Dae-Shik Kim
    Pages 43-53

  6. Semi-supervised Cooperative Learning for Multiomics Data Fusion

    • Daisy Yi Ding, Xiaotao Shen, Michael Snyder, Robert Tibshirani
    Pages 54-63

  7. Exploiting Partial Common Information Microstructure for Multi-modal Brain Tumor Segmentation

    • Yongsheng Mei, Guru Venkataramani, Tian Lan
    Pages 64-85

  8. Multimodal LLMs for Health Grounded in Individual-Specific Data

    • Anastasiya Belyaeva, Justin Cosentino, Farhad Hormozdiari, Krish Eswaran, Shravya Shetty, Greg Corrado et al.
    Pages 86-102

  9. Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders

    • Farnaz Khun Jush, Peter M. Dueppenbecker, Andreas Maier
    Pages 103-114

  10. HOOREX: Higher Order Optimizers for 3D Recovery from X-Ray Images

    • Karthik Shetty, Annette Birkhold, Bernhard Egger, Srikrishna Jaganathan, Norbert Strobel, Markus Kowarschik et al.
    Pages 115-124

  11. GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

    • Debesh Jha, Vanshali Sharma, Neethi Dasu, Nikhil Kumar Tomar, Steven Hicks, M. K. Bhuyan et al.
    Pages 125-140

  12. MaxCorrMGNN: A Multi-graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction

    • Niharika S. D’Souza, Hongzhi Wang, Andrea Giovannini, Antonio Foncubierta-Rodriguez, Kristen L. Beck, Orest Boyko et al.
    Pages 141-154

  13. SIM-CNN: Self-supervised Individualized Multimodal Learning for Stress Prediction on Nurses Using Biosignals

    • Sunmin Eom, Sunwoo Eom, Peter Washington
    Pages 155-171

  14. InterSynth: A Semi-Synthetic Framework for Benchmarking Prescriptive Inference from Observational Data

    • Dominic Giles, Robert Gray, Chris Foulon, Guilherme Pombo, Tianbo Xu, James K. Ruffle et al.
    Pages 172-188

  15. Back Matter

    Pages 189-190
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Other volumes

  1. Machine Learning for Multimodal Healthcare Data

Keywords

About this book

This book constitutes the proceedings of the First International Workshop on Machine Learning for Multimodal Healthcare Date, ML4MHD 2023, held in Honolulu, Hawaii, USA, in July 2023. 
The 18 full papers presented were carefully reviewed and selected from 30 submissions. The workshop's primary objective was to bring together experts from diverse fields such as medicine, pathology, biology, and machine learning. With the aim to present novel methods and solutions that address healthcare challenges, especially those that arise from the complexity and heterogeneity of patient data.


Editors and Affiliations

  • Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    Andreas K. Maier

  • Technical University of Munich, Garching bei München, Germany

    Julia A. Schnabel

  • College of Engineering, University of Wisconsin–Madison, Madison, USA

    Pallavi Tiwari

  • German Cancer Research Center, Heidelberg, Germany

    Oliver Stegle

Bibliographic Information

  • Book Title: Machine Learning for Multimodal Healthcare Data

  • Book Subtitle: First International Workshop, ML4MHD 2023, Honolulu, Hawaii, USA, July 29, 2023, Proceedings

  • Editors: Andreas K. Maier, Julia A. Schnabel, Pallavi Tiwari, Oliver Stegle

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-47679-2

  • 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: 978-3-031-47678-5Published: 26 November 2023

  • eBook ISBN: 978-3-031-47679-2Published: 25 November 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 190

  • Number of Illustrations: 6 b/w illustrations, 38 illustrations in colour

  • Topics: Health Informatics

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