106 citations · 241 across the 13 of their papers we have counts for
13 papers
Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples
Sai Spandana Chintapalli, Rongguang Wang, Zhijian Yang +7
Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disea…
Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning
Junhao Wen, Mathilde Antoniades, Zhijian Yang +4
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neu…
Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical Neuroscience
Rongguang Wang, Guray Erus, Pratik Chaudhari +1
Machine learning (ML) has shown great promise for revolutionizing a number of areas, including healthcare. However, it is also facing a reproducibility crisis, especially in medici…
DAGrid: Directed Accumulator Grid
Hang Zhang, Renjiu Hu, Xiang Chen +3
Recent research highlights that the Directed Accumulator (DA), through its parametrization of geometric priors into neural networks, has notably improved the performance of medical…
DeDA: Deep Directed Accumulator
Hang Zhang, Rongguang Wang, Renjiu Hu +2
Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps. Th…
Spatially Covariant Lesion Segmentation
Hang Zhang, Rongguang Wang, Jinwei Zhang +3
Compared to natural images, medical images usually show stronger visual patterns and therefore this adds flexibility and elasticity to resource-limited clinical applications by inj…
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
Rongguang Wang, Vishnu Bashyam, Zhijian Yang +10
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called…
Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies
Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, a…
Harmonization with Flow-based Causal Inference
Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
Heterogeneity in medical data, e.g., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using…
NeRD: Neural Representation of Distribution for Medical Image Segmentation
Hang Zhang, Rongguang Wang, Jinwei Zhang +5
We introduce Neural Representation of Distribution (NeRD) technique, a module for convolutional neural networks (CNNs) that can estimate the feature distribution by optimizing an u…
Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation
Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In med…
Geometric Loss for Deep Multiple Sclerosis lesion Segmentation
Hang Zhang, Jinwei Zhang, Rongguang Wang +5
Multiple sclerosis (MS) lesions occupy a small fraction of the brain volume, and are heterogeneous with regards to shape, size and locations, which poses a great challenge for trai…
Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation
Hang Zhang, Jinwei Zhang, Rongguang Wang +4
Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been fu…