Automatic Endoscopic Ultrasound Station Recognition with Limited Data

Pancreatic cancer is a lethal form of cancer that significantly contributes to cancer-related deaths worldwide. Early detection is essential to improve patient prognosis and survival rates. Despite advances in medical imaging techniques, pancreatic cancer remains a challenging disease to detect. Endoscopic ultrasound (EUS) is the most effective diagnostic tool for detecting pancreatic cancer. However, it requires expert interpretation of complex ultrasound images to complete a reliable patient scan. To obtain complete imaging of the pancreas, practitioners must learn to guide the endoscope into multiple "EUS stations" (anatomical locations), which provide different views of the pancreas. This is a difficult skill to learn, involving over 225 proctored procedures with the support of an experienced doctor. We build an AI-assisted tool that utilizes deep learning techniques to identify these stations of the stomach in real time during EUS procedures. This computer-assisted diagnostic (CAD) will help train doctors more efficiently. Historically, the challenge faced in developing such a tool has been the amount of retrospective labeling required by trained clinicians. To solve this, we developed an open-source user-friendly labeling web app that streamlines the process of annotating stations during the EUS procedure with minimal effort from the clinicians. Our research shows that employing only 43 procedures with no hyperparameter fine-tuning obtained a balanced accuracy of 90%, comparable to the current state of the art. In addition, we employ Grad-CAM, a visualization technology that provides clinicians with interpretable and explainable visualizations

Feature Importance Guided Attack: A Model Agnostic Adversarial Attack

Machine learning models are susceptible to adversarial attacks which dramatically reduce their performance. Reliable defenses to these attacks are an unsolved challenge. In this work, we present a novel evasion attack: the 'Feature Importance Guided Attack' (FIGA) which generates adversarial evasion samples. FIGA is model agnostic, it assumes no prior knowledge of the defending model's learning algorithm, but does assume knowledge of the feature representation. FIGA leverages feature importance rankings; it perturbs the most important features of the input in the direction of the target class we wish to mimic. We demonstrate FIGA against eight phishing detection models. We keep the attack realistic by perturbing phishing website features that an adversary would have control over. Using FIGA we are able to cause a reduction in the F1-score of a phishing detection model from 0.96 to 0.41 on average. Finally, we implement adversarial training as a defense against FIGA and show that while it is sometimes effective, it can be evaded by changing the parameters of FIGA