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Trustworthy Machine Learning for Healthcare

First International Workshop, TML4H 2023, Virtual Event, May 4, 2023, Proceedings

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Hao Chen

    1. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

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  2. Luyang Luo

    1. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13932)

Included in the following conference series:

Conference proceedings info: TML4H 2023.

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Table of contents (16 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Do Tissue Source Sites Leave Identifiable Signatures in Whole Slide Images Beyond Staining?

    • Piotr Keller, Muhammad Dawood, Fayyaz ul Amir Minhas
    Pages 1-10

  3. Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography

    • Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cedric Archambeau, Tamas Madl et al.
    Pages 11-24

  4. Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives

    • Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos, Farah E. Shamout
    Pages 25-40

  5. Self-supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-Grained Time Resolution

    • Kwanhyung Lee, John Won, Heejung Hyun, Sangchul Hahn, Edward Choi, Joohyung Lee
    Pages 41-50

  6. Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants

    • Isabel Chien, Javier Gonzalez Hernandez, Richard E. Turner
    Pages 51-59

  7. CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs

    • Konstantin Hemker, Zohreh Shams, Mateja Jamnik
    Pages 60-72

  8. ExBEHRT: Extended Transformer for Electronic Health Records

    • Maurice Rupp, Oriane Peter, Thirupathi Pattipaka
    Pages 73-84

  9. STASIS: Reinforcement Learning Simulators for Human-Centric Real-World Environments

    • Georgios Efstathiadis, Patrick Emedom-Nnamdi, Arinbjörn Kolbeinsson, Jukka-Pekka Onnela, Junwei Lu
    Pages 85-92

  10. Cross-Domain Microscopy Cell Counting By Disentangled Transfer Learning

    • Zuhui Wang
    Pages 93-105

  11. Post-hoc Saliency Methods Fail to Capture Latent Feature Importance in Time Series Data

    • Maresa Schröder, Alireza Zamanian, Narges Ahmidi
    Pages 106-121

  12. Enhancing Healthcare Model Trustworthiness Through Theoretically Guaranteed One-Hidden-Layer CNN Purification

    • Hanxiao Lu, Zeyu Huang, Ren Wang
    Pages 122-133

  13. A Kernel Density Estimation Based Quality Metric for Quality Assessment of Obstetric Ultrasound Video

    • Jong Kwon, Jianbo Jiao, Alice Self, Julia Alison Noble, Aris Papageorghiou
    Pages 134-146

  14. Learn2Agree: Fitting with Multiple Annotators Without Objective Ground Truth

    • Chongyang Wang, Yuan Gao, Chenyou Fan, Junjie Hu, Tin Lum Lam, Nicholas D. Lane et al.
    Pages 147-162

  15. Conformal Prediction Masks: Visualizing Uncertainty in Medical Imaging

    • Gilad Kutiel, Regev Cohen, Michael Elad, Daniel Freedman, Ehud Rivlin
    Pages 163-176

  16. Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition Through the Lens of Robustness

    • Yanqi Cheng, Lihao Liu, Shujun Wang, Yueming Jin, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero
    Pages 177-189

  17. Geometry-Based End-to-End Segmentation of Coronary Artery in Computed Tomography Angiography

    • Xiaoyu Yang, Lijian Xu, Simon Yu, Qing Xia, Hongsheng Li, Shaoting Zhang
    Pages 190-196

  18. Back Matter

    Pages 197-198
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  1. Trustworthy Machine Learning for Healthcare

Keywords

About this book

This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023.

The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.

Editors and Affiliations

  • Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Hao Chen, Luyang Luo

Bibliographic Information

  • Book Title: Trustworthy Machine Learning for Healthcare

  • Book Subtitle: First International Workshop, TML4H 2023, Virtual Event, May 4, 2023, Proceedings

  • Editors: Hao Chen, Luyang Luo

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-39539-0

  • 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: 978-3-031-39538-3Published: 31 July 2023

  • eBook ISBN: 978-3-031-39539-0Published: 30 July 2023

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 198

  • Number of Illustrations: 8 b/w illustrations, 60 illustrations in colour

  • Topics: Machine Learning

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