11 citations · 22 across the 10 of their papers we have counts for
10 papers
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…
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…
TransLK-Net: Entangling Transformer and Large Kernel for Progressive and Collaborative Feature Encoding and Decoding in Medical Image Segmentation
Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
Convolutional neural networks (CNNs) and vision transformers (ViTs) are widely employed for medical image segmentation, but they are still challenged by their intrinsic characteris…
Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
Medical vision foundation models remain limited in downstream tasks, particularly volumetric medical image segmentation. While fine-tuning on labeled target-domain data improves pe…
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…
DMC-Net: Lightweight Dynamic Multi-Scale and Multi-Resolution Convolution Network for Pancreas Segmentation in CT Images
Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
Convolutional neural networks (CNNs) have shown great effectiveness in medical image segmentation. However, they may be limited in modeling large inter-subject variations in organ…
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.…
Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentation
Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
U-Net has been widely used for segmenting abdominal organs, achieving promising performance. However, when it is used for multi-organ segmentation, first, it may be limited in expl…
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-…