activity
20182026
most citedA multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images

4 citations · 8 across the 8 of their papers we have counts for

collaborators

9 papers

cs.AI2026

Medi-Gemma: A Hybrid Clinical Decision Support System Integrating Deterministic EMR Analytics and Retrieval-Augmented Generation

Mohammed Saim Ahmed Quadri, Yunzhe Xue, Justin W. Ady +1

Deploying Large Language Models (LLMs) in high-stakes clinical settings remains limited by structural hallucinations, weak deterministic reasoning over tabular patient data, and om…

cs.CV2026

Evaluation of Medical Vision Language Models HuluMed and MedGemma, and general purpose chatbots Gemma 3, ChatGPT Plus, and Claude Pro on real previously unseen wound images

Yunzhe Xue, Mohammed Saim Ahmed Quadri, Neal Panse +2

Chronic wound assessment remains a clinically challenging task that requires accurate interpretation of wound morphology, tissue composition, vascular characteristics, and infectio…

cs.LG20202 cited

Defending against substitute model black box adversarial attacks with the 01 loss

Yunzhe Xue, Meiyan Xie, Usman Roshan

Substitute model black box attacks can create adversarial examples for a target model just by accessing its output labels. This poses a major challenge to machine learning models i…

cs.LG2020

Towards adversarial robustness with 01 loss neural networks

Yunzhe Xue, Meiyan Xie, Usman Roshan

Motivated by the general robustness properties of the 01 loss we propose a single hidden layer 01 loss neural network trained with stochastic coordinate descent as a defense agains…

cs.LG20201 cited

On the transferability of adversarial examples between convex and 01 loss models

Yunzhe Xue, Meiyan Xie, Usman Roshan

The 01 loss gives different and more accurate boundaries than convex loss models in the presence of outliers. Could the difference of boundaries translate to adversarial examples t…

cs.LG20201 cited

Robust binary classification with the 01 loss

Yunzhe Xue, Meiyan Xie, Usman Roshan

The 01 loss is robust to outliers and tolerant to noisy data compared to convex loss functions. We conjecture that the 01 loss may also be more robust to adversarial attacks. To st…