63 citations · 202 across the 28 of their papers we have counts for
28 papers
Interpretability-Aware Vision Transformer
Yao Qiang, Chengyin Li, Prashant Khanduri +1
Vision Transformers (ViTs) have become prominent models for solving various vision tasks. However, the interpretability of ViTs has not kept pace with their promising performance.…
AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation
Chengyin Li, Prashant Khanduri, Yao Qiang +3
Segment Anything Model (SAM) is one of the pioneering prompt-based foundation models for image segmentation and has been rapidly adopted for various medical imaging applications. H…
Fairness-aware Vision Transformer via Debiased Self-Attention
Yao Qiang, Chengyin Li, Prashant Khanduri +1
Vision Transformer (ViT) has recently gained significant attention in solving computer vision (CV) problems due to its capability of extracting informative features and modeling lo…
Negative Flux Aggregation to Estimate Feature Attributions
Xin Li, Deng Pan, Chengyin Li +2
There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns. Due to multi-layer nonlinearity of th…
Learning Compact Features via In-Training Representation Alignment
Xin Li, Xiangrui Li, Deng Pan +2
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i.e., last hidden layer) and a linear classifier (i.e., output layer) that…
Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation
Chengyin Li, Zheng Dong, Nathan Fisher +1
Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to all…
FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images
Chengyin Li, Yao Qiang, Rafi Ibn Sultan +4
Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissu…
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
Xin Li, Yao Qiang, Chengyin Li +2
This work tackles a central machine learning problem of performance degradation on out-of-distribution (OOD) test sets. The problem is particularly salient in medical imaging based…
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification
Yao Qiang, Supriya Tumkur Suresh Kumar, Marco Brocanelli +1
On-device Deep Neural Networks (DNNs) have recently gained more attention due to the increasing computing power of the mobile devices and the number of applications in Computer Vis…
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints
Xin Li, Xiangrui Li, Deng Pan +1
Convolutional neural networks (CNNs) have achieved state-of-the-art performance on various tasks in computer vision. However, recent studies demonstrate that these models are vulne…
Defending against adversarial attacks on medical imaging AI system, classification or detection?
Xin Li, Deng Pan, Dongxiao Zhu
Medical imaging AI systems such as disease classification and segmentation are increasingly inspired and transformed from computer vision based AI systems. Although an array of adv…
COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays
Xin Li, Chengyin Li, Dongxiao Zhu
During the COVID-19 pandemic, there has been an emerging need for rapid, dedicated, and point-of-care COVID-19 patient disposition techniques to optimize resource utilization and c…
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
Yao Qiang, Xin Li, Dongxiao Zhu
Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representati…
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
Xiangrui Li, Xin Li, Deng Pan +1
Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training d…
Improve SGD Training via Aligning Mini-batches
Xiangrui Li, Deng Pan, Xin Li +1
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of a feature extractor (i.e. last hidden layer) and a linear classifier (i.e. output layer) that is…
Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study
Najibesadat Sadati, Milad Zafar Nezhad, Ratna Babu Chinnam +1
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by…
Tackling Ordinal Regression Problem for Heterogeneous Data: Sparse and Deep Multi-Task Learning Approaches
Lu Wang, Dongxiao Zhu
Many real-world datasets are labeled with natural orders, i.e., ordinal labels. Ordinal regression is a method to predict ordinal labels that finds a wide range of applications in…
Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks
Xiangrui Li, Jasmine Hect, Moriah Thomason +1
Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in fu…
Vispi: Automatic Visual Perception and Interpretation of Chest X-rays
Xin Li, Rui Cao, Dongxiao Zhu
Medical imaging contains the essential information for rendering diagnostic and treatment decisions. Inspecting (visual perception) and interpreting image to generate a report are…
CRCEN: A Generalized Cost-sensitive Neural Network Approach for Imbalanced Classification
Xiangrui Li, Dongxiao Zhu
Classification on imbalanced datasets is a challenging task in real-world applications. Training conventional classification algorithms directly by minimizing classification error…
Multi-task Prediction of Patient Workload
Mohammad Hessam Olya, Dongxiao Zhu, Kai Yang
Developing reliable workload predictive models can affect many aspects of clinical decision making procedure. The primary challenge in healthcare systems is handling the demand unc…
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer
Milad Zafar Nezhad, Najibesadat Sadati, Kai Yang +1
Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are…
Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study
Najibesadat Sadati, Milad Zafar Nezhad, Ratna Babu Chinnam +1
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by…
SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
Milad Zafar Nezhad, Dongxiao Zhu, Najibesadat Sadati +2
Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different pa…