11 citations · 31 across the 15 of their papers we have counts for
15 papers
Active Source-free Domain Adaptation in Open-set Medical Image Segmentation via Decomposed Uncertainty and Prototype Discrepancy
Jin Yang, Yichi Zhang, Peijie Qiu +1
Deep learning (DL) methods are challenged to demonstrate robust performance across different segmentation datasets due to domain shifts, but active domain adaptation techniques enh…
FEFormer: Frequency-enhanced Vision Transformer for Generic Knowledge Extraction and Adaptive Feature Fusion in Volumetric Medical Image Segmentation
Jin Yang, Xiaobing Yu, Peijie Qiu
Accurate segmentation of organs and lesions in medical images is essential for clinical applications including diagnosis, prognosis, and treatment planning. While Vision Transforme…
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…
D2-MLP: Dynamic Decomposed MLP Mixer for Medical Image Segmentation
Jin Yang, Xiaobing Yu, Peijie Qiu
Convolutional neural networks are widely used in various segmentation tasks in medical images. However, they are challenged to learn global features adaptively due to the inherent…
SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks
Xiaotong Sun, Peijie Qiu, Shengfan Zhang
Survival analysis models time-to-event distributions with censorship. Recently, deep survival models using neural networks have dominated due to their representational power and st…
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification
Wenhui Zhu, Xiwen Chen, Peijie Qiu +3
Multiple instance learning (MIL) stands as a powerful approach in weakly supervised learning, regularly employed in histological whole slide image (WSI) classification for detectin…
SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation
Wenhui Zhu, Xiwen Chen, Peijie Qiu +4
Since its introduction, UNet has been leading a variety of medical image segmentation tasks. Although numerous follow-up studies have also been dedicated to improving the performan…
AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation
Peijie Qiu, Jin Yang, Sayantan Kumar +2
In the past decades, deep neural networks, particularly convolutional neural networks, have achieved state-of-the-art performance in a variety of medical image segmentation tasks.…
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation
Jin Yang, Peijie Qiu, Yichi Zhang +2
Hierarchical transformers have achieved significant success in medical image segmentation due to their large receptive field and capabilities of effectively leveraging global long-…
SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA
Pan Xiao, Peijie Qiu, Sungmin Ha +3
Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. Several variants of variational autoencoder…