Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors

Chang, Enoch; Joel, Marina; Chang, Hannah; Du, Justin; Khanna, Omaditya; Omuro, Antonio; Chiang, Veronica; Aneja, Sanjay

doi:10.1101/2020.11.04.20226159

Cited by 1 publication

(3 citation statements)

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“…MRI images were obtained from a MRI dataset of 831 patients with 3,596 brain metastases treated with primary stereotactic radiosurgery (SRS) at our institution between 2000-2018 [29]. Patients with prior resection or prior radiation treatment were excluded along with metastases <5 mm.…”

Section: Methodsmentioning

confidence: 99%

“…MRI data consisted of brain MRIs from 831 patients from a single institution brain metastases registry (22). The presence or absence of a malignancy was identified on 4,000 brain lesions seen on MRI.…”

Section: Methodsmentioning

confidence: 99%

“…We obtained 1696 scanned film mammograms from the CBIS-DDSM and split the images into two subsets for training (1318 images) and testing (378 images). MRI images were obtained from a MRI dataset of 831 patients with 3,596 brain metastases treated with primary stereotactic radiosurgery (SRS) at our institution between 2000-2018 [29].…”

Section: Ct Images Were Acquired From the Lung Image Database Consortium And Image Databasementioning

confidence: 99%

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Using Adversarial Images to Assess the Stability of Deep Learning Models Trained on Diagnostic Images in Oncology

Joel

Umrao

Chang

et al. 2021

Preprint

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BackgroundDeep learning (DL) models have shown the ability to automate the classification of medical images used for cancer detection. Unfortunately, recent studies have found that DL models are vulnerable to adversarial attacks which manipulate models into making incorrect predictions with high confidence. There is a need for better understanding of how adversarial attacks impact the predictive ability of DL models in the medical image domain.MethodsWe studied the adversarial attack susceptibility of DL models for three common imaging tasks within oncology. We investigated how PGD adversarial training could be employed to increase model robustness against FGSM, PGD, and BIM attacks. Finally, we studied the utility of adversarial sensitivity as a metric to improve model performance.ResultsOur experiments showed that medical DL models were highly sensitive to adversarial attacks, as visually imperceptible degrees of perturbation (<0.004) were sufficient to deceive the model the majority of the time. DL models for medical images were more vulnerable to adversarial attacks compared to DL models for non-medical images. Adversarial training increased model performance on adversarial samples for all classification tasks. We were able to increase model accuracy on clean images for all datasets by excluding images most vulnerable to adversarial perturbation.ConclusionOur results indicated that while medical DL systems are extremely susceptible to adversarial attacks, adversarial training show promise as an effective defense against attacks. Adversarial susceptibility of individual images can be used to increase model performance by identifying images most at-risk for misclassification. Our findings provide a useful basis for designing more robust and accurate medical DL models as well as techniques to defend models from adversarial attack.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Ct Images Were Acquired From the Lung Image Database Consortium And Image Databasementioning

confidence: 99%

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