Ethics and Fairness in Medical Imaging : Second International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and Third International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6–10, 2024, Proceedings / edited by Esther Puyol-Antón, Ghada Zamzmi, Aasa Feragen, Andrew P. King, Veronika Cheplygina, Melanie Ganz-Benjaminsen, Enzo Ferrante, Ben Glocker, Eike Petersen, John S. H. Baxter, Islem Rekik, Roy Eagleson.

Format:

Book

Contributor:

Puyol Anton, Esther, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 15198

Language:

English

Subjects (All):

Computer vision.

Machine learning.

Artificial intelligence.

Computer networks.

Information technology--Management.

Information technology.

Computer Vision.

Machine Learning.

Artificial Intelligence.

Computer Communication Networks.

Computer Application in Administrative Data Processing.

Local Subjects:

Computer Vision.

Machine Learning.

Artificial Intelligence.

Computer Communication Networks.

Computer Application in Administrative Data Processing.

Physical Description:

1 online resource (202 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

This book constitutes the refereed proceedings of the Second International Workshop, FAIMI 2024, and the Third International Workshop, EPIMI 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, in October 2024. The 17 full papers presented in this book were carefully reviewed and selected from 21 submissions. FAIMI aimed to raise awareness about potential fairness issues in machine learning within the context of biomedical image analysis. The instance of EPIMI concentrates on topics surrounding open science, taking a critical lens on the subject.

Contents:

FAIMI: Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods

Dataset Distribution Impacts Model Fairness: Single vs Multi-Task Learning

AI Fairness in Medical Imaging: Controlling for Disease Severity

Fair and Private CT Contrast Agent Detection

Mitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Disease Diagnosis for the Elderly

Fair AI Outcomes Without Sacrificing Group Gains

All you need is a guiding hand: mitigating shortcut bias in deep learning models for medical imaging

Exploring Fairness in State-of-the-Art Pulmonary Nodule Detection Algorithms

Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation

BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning

Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data

Do sites benefit equally from distributed learning in medical image analysis

Cycle-GANs generated difference maps to interpret race prediction from medical images

On Biases in a UK Biobank-based Retinal Image Classification Model

Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors

EPIMI: Assessing the Impact of Sociotechnical Harms in AI-based Medical Image Analysis

Practical and Ethical Considerations for Generative AI in Medical Imaging.

Notes:

Includes bibliographical references and index.

ISBN:

3-031-72787-8