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

Daya Kumar, Abhijith Sharma, Apurva Narayan

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.