An automatic pipeline for data shift detection and mitigation to improve outcome prediction of traumatic brain injury

Jiren Li; Dooman Arefan; Matthew Pease; Chang Liu; David O. Okonkwo; Shandong Wu

doi:10.1117/12.3009348

2 April 2024 An automatic pipeline for data shift detection and mitigation to improve outcome prediction of traumatic brain injury

Jiren Li, Dooman Arefan, Matthew Pease, Chang Liu, David O. Okonkwo, Shandong Wu

Author Affiliations +

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 129310C (2024) https://doi.org/10.1117/12.3009348
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Data shift, also known as dataset shift, is a prevalent concern in the field of machine learning. It occurs when the distribution of the data used for training a machine learning model is different from the distribution of the data the model will encounter in a real-world, operational environment (i.e., test set). This issue becomes even more significant in the field of medical imaging due to the multitude of factors that can contribute to data shifts. It is crucial for medical machine learning systems to identify and address these issues. In this paper, we present an automated pipeline designed to identify and alleviate certain types of data shift issues in medical imaging datasets. We intentionally introduce data shift into our dataset to assess and address it within our workflow. More specifically, we employ Principal Components Analysis (PCA) and Maximum Mean Discrepancy (MMD) algorithms to detect data shift between the training and test datasets. We utilize image processing techniques, including data augmentation and image registration methods, to individually and collectively mitigate data shift issues and assess their impacts. In the experiments we use a head CT image dataset of 537 patients with severe traumatic brain injury (sTBI) for patient outcome prediction. Results show that our proposed method is effective in detecting and significantly improving model performance.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiren Li, Dooman Arefan, Matthew Pease, Chang Liu, David O. Okonkwo, and Shandong Wu "An automatic pipeline for data shift detection and mitigation to improve outcome prediction of traumatic brain injury", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 129310C (2 April 2024); https://doi.org/10.1117/12.3009348

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available