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Toward Fair Ultrasound Computing Tomography: Challenges, Solutions and Outlook

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Lei YangAuthors Info & Claims

GLSVLSI '24: Proceedings of the Great Lakes Symposium on VLSI 2024

Pages 748 - 753

https://doi.org/10.1145/3649476.3660387

Published: 12 June 2024 Publication History

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Abstract

Medical image reconstruction plays a pivotal role in early cancer detection, which can significantly enhance both the quality and longevity of a patient’s life through timely treatment. However, the extent to which current image reconstruction methods accurately represent all populations, and whether they underperform for certain groups, remains largely unexplored. In this work, we will examine the deep learning (DL)–based approach to image reconstruction and its associated fairness concerns. Initially, our experiments confirmed the unfairness’s presence. Subsequently, by addressing the issue from two perspectives, we gained valuable insights, which deepened our understanding of the problem. To assess a model’s fairness, it’s crucial to evaluate it from various perspectives, as relying on a single metric can often yield misleading results.

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Cited By

Sheng YYang JWang HFeng YChen YGuo XLin YJiang WYang L(2024)Tutorial on Novel Toolkits toward AI for Science on Resource-Constrained Computing Systems2024 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)10.1109/CODES-ISSS60120.2024.00008(3-4)Online publication date: 29-Sep-2024
https://doi.org/10.1109/CODES-ISSS60120.2024.00008

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Published In

cover image ACM Conferences

GLSVLSI '24: Proceedings of the Great Lakes Symposium on VLSI 2024

June 2024

797 pages

ISBN:9798400706059

DOI:10.1145/3649476

Editors:
Inna Partin-Vaisband
University of Illinois Chicago, USA
,
Srinivas Katkoori
University of South Florida, USA
,
Lu Peng
Tulane University, USA
,
Boris Vaisband
McGill University, Canada
,
Tooraj Nikoubin
University of Texas at Dallas, USA

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGDA: ACM Special Interest Group on Design Automation

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Association for Computing Machinery

New York, NY, United States

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Published: 12 June 2024

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GLSVLSI '24: Great Lakes Symposium on VLSI 2024

June 12 - 14, 2024

FL, Clearwater, USA

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Overall Acceptance Rate 312 of 1,156 submissions, 27%

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Cited By

Sheng YYang JWang HFeng YChen YGuo XLin YJiang WYang L(2024)Tutorial on Novel Toolkits toward AI for Science on Resource-Constrained Computing Systems2024 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)10.1109/CODES-ISSS60120.2024.00008(3-4)Online publication date: 29-Sep-2024
https://doi.org/10.1109/CODES-ISSS60120.2024.00008

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