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Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting.

Eric J Snider¹, Sofia I Hernandez-Torres¹, Ryan Hennessey¹

¹U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA.

Diagnostics (Basel, Switzerland)

|February 11, 2023

Summary

Deep learning models for medical imaging diagnostics can be improved. Data augmentation and ensemble methods boosted shrapnel identification accuracy in ultrasound images to over 85%.

Keywords:

artificial intelligence data augmentation deep learning ensemble predictions image classification machine learning medical imaging overfitting shrapnel ultrasound imaging

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Area of Science:

Medical imaging
Artificial intelligence
Diagnostic tools

Background:

Deep learning models offer potential for automating medical imaging diagnostics.
Generalizability across subject variability is crucial for clinical application.
Previous ShrapML model showed <70% accuracy for shrapnel identification.

Purpose of the Study:

To improve the test accuracy and generalizability of a deep learning image classifier for shrapnel identification.
To evaluate the impact of data augmentation and ensemble methods on model performance.

Main Methods:

Utilized a neural network (ShrapML) with leave-one-subject-out (LOSO) cross-validation.
Implemented image augmentation techniques (affine transformations) and MixUp for generating training data.
Employed ensemble prediction strategies, including bagging confidences/predictions from multiple LOSO holdouts.

Main Results:

Initial ShrapML accuracy was <70%, with variability.
Data augmentation and MixUp improved accuracy to 75%.
Top-3 LOSO confidence bagging achieved >85% accuracy on blind tissue phantoms.
Gradient-weighted class activation mapping confirmed shrapnel tracking.

Conclusions:

Data augmentation and ensemble prediction approaches enhance the generalization of predictive models for ultrasound image analysis.
These methods are critical for developing reliable, real-time diagnostic deployment systems.
Improved accuracy and reliability are key for clinical adoption of AI in medical imaging.