Publications (17)
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification
Linhai Ma, Liang Liang
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the human heart. By using deep neural networks (DNNs), interpretation of ECG signals can…
SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI
Jiasong Chen, Linchen Qian, Linhai Ma +3
Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measur…
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective
Linhai Ma, Liang Liang
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many rese…
EPPCMinerBen: A Novel Benchmark for Evaluating Large Language Models on Electronic Patient-Provider Communication via the Patient Portal
Samah Fodeh, Yan Wang, Linhai Ma +3
Effective communication in health care is critical for treatment outcomes and adherence. With patient-provider exchanges shifting to secure messaging, analyzing electronic patient-…
PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data
Samah Fodeh, Linhai Ma, Yan Wang +6
Patient-generated text such as secure messages, surveys, and interviews contains rich expressions of the patient voice (PV), reflecting communicative behaviors and social determina…
Herculean: An Agentic Benchmark for Financial Intelligence
Xueqing Peng, Zhuohan Xie, Yupeng Cao +60
As AI agents improve, the central question is no longer whether they can solve isolated well-defined financial tasks, but whether they can reliably carry out financial professional…
Attention-based Shape-Deformation Networks for Artifact-Free Geometry Reconstruction of Lumbar Spine from MR Images
Linchen Qian, Jiasong Chen, Linhai Ma +3
Lumbar disc degeneration, a progressive structural wear and tear of lumbar intervertebral disc, is regarded as an essential role on low back pain, a significant global health conce…
Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval
Linhai Ma, Ethan F. Wei, Xueqing Peng +3
Large-taxonomy retrieval often assumes that the input already expresses the target concept. In many settings, however, the input is indirect evidence, such as a table cell whose me…
PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization
Samah Fodeh, Linhai Ma, Ganesh Puthiaraju +7
Motivation: Patient-generated text contains critical information on patients' lived experiences, social context, and care engagement, but remains largely unstructured, limiting its…
STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction
Samah Fodeh, Ganesh Puthiaraju, Elyas Irankhah +5
Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and…
PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models
Samah Fodeh, Linhai Ma, Ganesh Puthiaraju +5
Motivation: Patient-generated text contains critical information about patients' lived experiences, social circumstances, and engagement in care, including factors that strongly in…
EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages
Samah Fodeh, Sreeraj Ramachandran, Elyas Irankhah +7
Secure patient-provider messages contain clinically important communication behaviors that are difficult to characterize manually at scale. The Electronic Patient-Provider Communic…
TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation
Samah Fodeh, Linhai Ma, Ganesh Puthiaraju +8
Direct Preference Optimization (DPO) is effective for offline alignment but poorly matched to ontology-driven structured prediction, where preferred and rejected JSON often differ…
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks
Linhai Ma, Liang Liang
Deep neural networks (DNNs) are vulnerable to adversarial noises. Adversarial training is a general and effective strategy to improve DNN robustness (i.e., accuracy on noisy data)…
An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder
Liang Liang, Linhai Ma, Linchen Qian +1
Deep neural networks (DNNs), especially convolutional neural networks, have achieved superior performance on image classification tasks. However, such performance is only guarantee…
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection
Linhai Ma, Liang Liang
It is known that Deep Neural networks (DNNs) are vulnerable to adversarial attacks, and the adversarial robustness of DNNs could be improved by adding adversarial noises to trainin…
Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length
Linhai Ma, Liang Liang
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many rese…