Attacking CNNs in Histopathology with SNAP: Sporadic and Naturalistic Adversarial Patches (Student Abstract)

Convolutional neural networks (CNNs) are being increasingly

adopted in medical imaging. However, in the race for

developing accurate models, their robustness is often overlooked.

This elicits a significant concern given the safety-critical

nature of the healthcare system. Here, we highlight

the vulnerability of CNNs against a sporadic and naturalistic

adversarial patch attack (SNAP). We train SNAP to mislead

the ResNet50 model predicting metastasis in histopathological

scans of lymph node sections, lowering the accuracy by

27%. This work emphasizes the need for defense strategies

before deploying CNNs in critical healthcare settings.