papers

Publications (24)

eess.IV2021

Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details

Zhuqing Jiang, Chang Liu, Ya'nan Wang +4

With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography. Images capt…

cs.CV2022

Seeing your sleep stage: cross-modal distillation from EEG to infrared video

Jianan Han, Shaoxing Zhang, Aidong Men +4

It is inevitably crucial to classify sleep stage for the diagnosis of various diseases. However, existing automated diagnosis methods mostly adopt the "gold-standard" lectroencepha…

cs.CV2024

Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection

Zining Chen, Xingshuang Luo, Weiqiu Wang +3

Recent Anomaly Detection (AD) methods have achieved great success with In-Distribution (ID) data. However, real-world data often exhibits distribution shift, causing huge performan…

cs.CV2023

EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images

Yinsong Xu, Jiaqi Tang, Aidong Men +1

Medical image segmentation has immense clinical applicability but remains a challenge despite advancements in deep learning. The Segment Anything Model (SAM) exhibits potential in…

cs.CV2022

Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation

Jiangbo Pei, Zhuqing Jiang, Aidong Men +3

Source-free unsupervised domain adaptation (SFUDA) aims to learn a target domain model using unlabeled target data and the knowledge of a well-trained source domain model. Most pre…

eess.IV2024

Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI

Yinsong Xu, Yipei Wang, Ziyi Shen +7

The Gleason groups serve as the primary histological grading system for prostate cancer, providing crucial insights into the cancer's potential for growth and metastasis. In clinic…