collaborators

8 papers

cs.CV2025

BASIC: Semi-supervised Multi-organ Segmentation with Balanced Subclass Regularization and Semantic-conflict Penalty

Zhenghao Feng, Lu Wen, Yuanyuan Xu +4

Semi-supervised learning (SSL) has shown notable potential in relieving the heavy demand of dense prediction tasks on large-scale well-annotated datasets, especially for the challe…

eess.IV2024

S3PET: Semi-supervised Standard-dose PET Image Reconstruction via Dose-aware Token Swap

Jiaqi Cui, Pinxian Zeng, Yuanyuan Xu +3

To acquire high-quality positron emission tomography (PET) images while reducing the radiation tracer dose, numerous efforts have been devoted to reconstructing standard-dose PET (…

cs.CV2024

Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition

Yuxiang Yang, Lu Wen, Xinyi Zeng +4

Facial Expression Recognition (FER) holds significant importance in human-computer interactions. Existing cross-domain FER methods often transfer knowledge solely from a single lab…

cs.CV2024

Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding

Zhenghao Feng, Lu Wen, Jianghong Xiao +5

Deep learning (DL) has successfully automated dose distribution prediction in radiotherapy planning, enhancing both efficiency and quality. However, existing methods suffer from th…

cs.CV2024

Adaptive Prompt Learning with Negative Textual Semantics and Uncertainty Modeling for Universal Multi-Source Domain Adaptation

Yuxiang Yang, Lu Wen, Yuanyuan Xu +2

Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple labeled source domains to an unlabeled target domain under domain shifts (different data distrib…

eess.IV2024

Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness

Yuchen Fei, Yanmei Luo, Yan Wang +4

To obtain high-quality positron emission tomography (PET) while minimizing radiation exposure, a range of methods have been designed to reconstruct standard-dose PET (SPET) from co…