7 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…
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…
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…
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…