2 citations · 2 across the 4 of their papers we have counts for
8 papers
Multimodal normative modeling in Alzheimers Disease with introspective variational autoencoders
Sayantan Kumar, Peijie Qiu, Aristeidis Sotiras
Normative modeling learns a healthy reference distribution and quantifies subject-specific deviations to capture heterogeneous disease effects. In Alzheimers disease (AD), multimod…
U-Harmony: Enhancing Joint Training for Segmentation Models with Universal Harmonization
Weiwei Ma, Xiaobing Yu, Peijie Qiu +7
In clinical practice, medical segmentation datasets are often limited and heterogeneous, with variations in modalities, protocols, and anatomical targets across institutions. Exist…
FM-LoRA: Factorized Low-Rank Meta-Prompting for Continual Learning
Xiaobing Yu, Jin Yang, Xiao Wu +2
How to adapt a pre-trained model continuously for sequential tasks with different prediction class labels and domains and finally learn a generalizable model across diverse tasks i…
Multimodal Variational Autoencoder: a Barycentric View
Peijie Qiu, Wenhui Zhu, Sayantan Kumar +6
Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in pa…
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
Peijie Qiu, Satrajit Chakrabarty, Phuc Nguyen +2
Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tool…
STA-Unet: Rethink the semantic redundant for Medical Imaging Segmentation
Vamsi Krishna Vasa, Wenhui Zhu, Xiwen Chen +3
In recent years, significant progress has been made in the medical image analysis domain using convolutional neural networks (CNNs). In particular, deep neural networks based on a…