127 citations · 523 across the 58 of their papers we have counts for
8 papers · 1 filter
Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making
Junlin Yang, Nicha C. Dvornek, Fan Zhang +4
This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making. Our…
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang +1
Recurrent neural networks (RNNs) were designed for dealing with time-series data and have recently been used for creating predictive models from functional magnetic resonance imagi…
Decision Explanation and Feature Importance for Invertible Networks
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li +2
Deep neural networks are vulnerable to adversarial attacks and hard to interpret because of their black-box nature. The recently proposed invertible network is able to accurately r…
Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation
Junlin Yang, Nicha C. Dvornek, Fan Zhang +3
A deep learning model trained on some labeled data from a certain source domain generally performs poorly on data from different target domains due to domain shifts. Unsupervised d…
Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
Junlin Yang, Nicha C. Dvornek, Fan Zhang +4
Domain Adaptation (DA) has the potential to greatly help the generalization of deep learning models. However, the current literature usually assumes to transfer the knowledge from…
Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang +2
Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality a…
Invertible Network for Classification and Biomarker Selection for ASD
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li +2
Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification tas…
Graph Neural Network for Interpreting Task-fMRI Biomarkers
Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou +3
Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promis…