1k citations · 1.6k across the 27 of their papers we have counts for
27 papers
Learning Better Contrastive View from Radiologist's Gaze
Sheng Wang, Zixu Zhuang, Xi Ouyang +6
Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims to minimizing distances between positive pairs. These methods usually apply…
ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs
Zihao Zhao, Sheng Wang, Jinchen Gu +6
The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic process…
DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task
Honglin Xiong, Sheng Wang, Yitao Zhu +5
The recent progress of large language models (LLMs), including ChatGPT and GPT-4, in comprehending and responding to human instructions has been remarkable. Nevertheless, these mod…
Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks
Mianxin Liu, Jingyang Zhang, Yao Wang +7
Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. However, the key evidence from neuroimaging data for pathologica…
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models
Sheng Wang, Zihao Zhao, Xi Ouyang +2
Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice. For example, a large dialog LLM l…
Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection
Zhenrong Shen, Xi Ouyang, Bin Xiao +3
Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologis…
Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning
Chong Ma, Lin Zhao, Yuzhong Chen +15
Learning harmful shortcuts such as spurious correlations and biases prevents deep neural networks from learning the meaningful and useful representations, thus jeopardizing the gen…
Transformers in Medical Image Analysis: A Review
Kelei He, Chen Gan, Zhuoyuan Li +7
Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also…
Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution
Zixu Zhuang, Liping Si, Sheng Wang +8
Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible b…
Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis
Xi Ouyang, Srikrishna Karanam, Ziyan Wu +5
We consider the problem of abnormality localization for clinical applications. While deep learning has driven much recent progress in medical imaging, many clinical challenges are…
mr2NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography
Sheng Wang, Jiayu Huo, Xi Ouyang +5
Computer-aided diagnosis with deep learning techniques has been shown to be helpful for the diagnosis of the mammography in many clinical studies. However, the image styles of diff…
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
Xi Ouyang, Jiayu Huo, Liming Xia +15
The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 20…
An Auto-Context Deformable Registration Network for Infant Brain MRI
Dongming Wei, Sahar Ahmad, Yunzhi Huang +7
Deformable image registration is fundamental to longitudinal and population analysis. Geometric alignment of the infant brain MR images is challenging, owing to rapid changes in im…
A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency
Jiayu Huo, Liping Si, Xi Ouyang +6
Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders and requires early-stage diagnosis. Nowadays, the deep convolutional neural networks have achieved grea…
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
Feng Shi, Jun Wang, Jun Shi +6
(This paper was submitted as an invited paper to IEEE Reviews in Biomedical Engineering on April 6, 2020.) The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over…
Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth
Kai Xuan, Liping Si, Lichi Zhang +6
High-quality magnetic resonance (MR) image, i.e., with near isotropic voxel spacing, is desirable in various scenarios of medical image analysis. However, many MR acquisitions use…
A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image
Yuyu Guo, Lei Bi, Euijoon Ahn +3
Dynamic medical imaging is usually limited in application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce th…
Robust Brain Magnetic Resonance Image Segmentation for Hydrocephalus Patients: Hard and Soft Attention
Xuhua Ren, Jiayu Huo, Kai Xuan +3
Brain magnetic resonance (MR) segmentation for hydrocephalus patients is considered as a challenging work. Encoding the variation of the brain anatomical structures from different…
Deep Local Global Refinement Network for Stent Analysis in IVOCT Images
Yuyu Guo
Implantation of stents into coronary arteries is a common treatment option for patients with cardiovascular disease. Assessment of safety and efficacy of the stent implantation occ…
Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors
Dongming Wei, Sahar Ahmad, Jiayu Huo +7
Thermal ablation is a minimally invasive procedure for treat-ing small or unresectable tumors. Although CT is widely used for guiding ablation procedures, the contrast of tumors ag…
Dual Adversarial Learning with Attention Mechanism for Fine-grained Medical Image Synthesis
Dong Nie, Lei Xiang, Qian Wang +1
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and risk, the acquisition of certain image modalities c…
Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization
Xuhua Ren, Lichi Zhang, Qian Wang +1
Medical image segmentation is challenging especially in dealing with small dataset of 3D MR images. Encoding the variation of brain anatomical struc-tures from individual subjects…
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
Xuhua Ren, Lichi Zhang, Sahar Ahmad +5
Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with s…
Deep Morphological Simplification Network (MS-Net) for Guided Registration of Brain Magnetic Resonance Images
Dongming Wei, Zhengwang Wu, Gang Li +3
Objective: Deformable brain MR image registration is challenging due to large inter-subject anatomical variation. For example, the highly complex cortical folding pattern makes it…