The Open Kidney Ultrasound Data Set

Singla, Rohit; Ringstrom, Cailin; Hu, Grace; Lessoway, Victoria; Reid, Janice; Nguan, Christopher; Rohling, Robert

doi:10.1007/978-3-031-44521-7_15

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…For example, a recent study improved convolutional neural network model accuracy from 84% to 92% by including image normalization and affine transformations to the image set [25]. For ultrasound imaging specifically, augmentation strategies can help in identifying COVID-19 abnormalities in lung tissue [26] or creating a more generalized model for kidney tissue applications [27]. Further details on data augmentation strategies for medical imaging have been detailed in review articles [28,29].…”

Section: Overview Of Ai Strategies For Creating More Generalized Modelsmentioning

confidence: 99%

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

2023

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Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network—termed ShrapML—blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.

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