22 citations · 63 across the 11 of their papers we have counts for
11 papers
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
Xiaoshi Wu, Yiming Hao, Keqiang Sun +4
Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing eval…
X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models
Yixiong Chen, Li Liu, Chris Ding
This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation m…
Label-Free Liver Tumor Segmentation
Qixin Hu, Yixiong Chen, Junfei Xiao +4
We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intrig…
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
Yixiong Chen, Chunhui Zhang, Chris H. Q. Ding +1
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical dataset…
Rethinking Two Consensuses of the Transferability in Deep Learning
Yixiong Chen, Jingxian Li, Chris Ding +1
Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans. This ability is name…
Synthetic Tumors Make AI Segment Tumors Better
Qixin Hu, Junfei Xiao, Yixiong Chen +4
We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and tex…
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
Chunhui Zhang, Yixiong Chen, Li Liu +2
The self-supervised ultrasound (US) video model pretraining can use a small amount of labeled data to achieve one of the most promising results on US diagnosis. However, it does no…
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
Yixiong Chen, Li Liu, Jingxian Li +3
In medical image analysis, transfer learning is a powerful method for deep neural networks (DNNs) to generalize well on limited medical data. Prior efforts have focused on developi…
QDCNN: Quantum Dilated Convolutional Neural Network
Yixiong Chen
In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum…
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning
Yixiong Chen, Chunhui Zhang, Li Liu +4
Most deep neural networks (DNNs) based ultrasound (US) medical image analysis models use pretrained backbones (e.g., ImageNet) for better model generalization. However, the domain…
Probabilistic Forecasting with Temporal Convolutional Neural Network
Yitian Chen, Yanfei Kang, Yixiong Chen +1
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probab…