activity
20182020
most citedJointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

35 citations · 52 across the 5 of their papers we have counts for

collaborators

10 papers

cs.CV2020

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek +4

Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient…

eess.IV2019

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…

eess.IV201935 cited

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…

cs.LG20193 cited

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…

eess.IV2019

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…

eess.IV2019

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…