1 citations · 1 across the 8 of their papers we have counts for
8 papers
MedIQA: A Scalable Foundation Model for Prompt-Driven Medical Image Quality Assessment
Siyi Xun, Yue Sun, Jingkun Chen +5
Rapid advances in medical imaging technology underscore the critical need for precise and automated image quality assessment (IQA) to ensure diagnostic accuracy. Existing medical I…
MoEdit: On Learning Quantity Perception for Multi-object Image Editing
Yanfeng Li, Kahou Chan, Yue Sun +6
Multi-object images are prevalent in various real-world scenarios, including augmented reality, advertisement design, and medical imaging. Efficient and precise editing of these im…
DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution
Yuanbo Zhou, Xinlin Zhang, Wei Deng +4
We introduce DiffSteISR, a pioneering framework for reconstructing real-world stereo images. DiffSteISR utilizes the powerful prior knowledge embedded in pre-trained text-to-image…
Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations
Ruige Zong, Tao Wang, Chunwang Li +8
Familial cerebral cavernous malformation (FCCM) is a hereditary disorder characterized by abnormal vascular structures within the central nervous system. The FCCM lesions are often…
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Xiangyu Xiong, Yue Sun, Xiaohong Liu +10
Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On t…
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification
Xiangyu Xiong, Yue Sun, Xiaohong Liu +6
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative advers…
Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information
Yuanbo Zhou, Yuyang Xue, Jiang Bi +8
Real-world stereo image super-resolution has a significant influence on enhancing the performance of computer vision systems. Although existing methods for single-image super-resol…
Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation
Tao Wang, Yuanbin Chen, Xinlin Zhang +7
Supervised learning algorithms based on Convolutional Neural Networks have become the benchmark for medical image segmentation tasks, but their effectiveness heavily relies on a la…