Impact of Adversarial Attack on Pediatric Hip Ultrasound Deep Learning Models
Bibliographic record
Abstract
Adversarial attacks examine the vulnerability of machine learning models to images corrupted by varying levels of perturbations. Typically, these images are visually indistinguishable and can be used to evaluate the robustness of a given model to noise. Adversarial images can also be included in the training set to improve the robustness of the model. We examine adversarial attacks on classification models trained on pediatric hip ultrasound images and use these to improve model robustness in scan adequacy assessment. Three methods of white box adversarial attack-Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Basic Iterative Method (BIM)-were applied to classification networks trained on 2D pediatric hip ultrasound images. We trained popular convolutional neural network(CNN) models like AlexNet, ResNet, DenseNet, Inception, and VGG two hip image datasets(DS) from 108 (DS1) and 200(DS2) subjects. The effect of the adversarial attack was evaluated based on the reduction in accuracy. The images generated were used for adversarial training to refine the CNN models. All deep learning models were sensitive to even mild perturbations (eps=0.2), imperceptible to the human eye. Accuracy of DL models reduced by 11-37% with the highest drop in observed in the DenseNet model. Upon validation on DS2 the accuracy of DL models improved by 2-6% with adversarial trainingClinical Relevance- This work applies adversarial attacks to deep learning models trained on b-mode hip ultrasound images. Initial results suggest that mild perturbations to the ultrasound image data can result in significant changes in the predictions of classification models, and that the robustness of these models improves when adversarial training is applied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".