Publications (59)
Multimodal Neurodegenerative Disease Subtyping Explained by ChatGPT
Diego Machado Reyes, Hanqing Chao, Juergen Hahn +2
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease; yet its currently available treatments are limited to stopping disease progression. Moreover, effectivenes…
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
Qingsong Yang, Pingkun Yan, Yanbo Zhang +5
In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein d…
Artificial General Intelligence for Medical Imaging Analysis
Xiang Li, Lin Zhao, Lu Zhang +16
Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general…
Chest X-ray Foundation Model with Global and Local Representations Integration
Zefan Yang, Xuanang Xu, Jiajin Zhang +3
Chest X-ray (CXR) is the most frequently ordered imaging test, supporting diverse clinical tasks from thoracic disease detection to postoperative monitoring. However, task-specific…
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Grant Haskins, Jochen Kruecker, Uwe Kruger +4
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressi…
X-WIN: Building Chest Radiograph World Model via Predictive Sensing
Zefan Yang, Ge Wang, James Hendler +2
Chest X-ray radiography (CXR) is an essential medical imaging technique for disease diagnosis. However, as 2D projectional images, CXRs are limited by structural superposition and…
Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review
Erim Yanik, Xavier Intes, Uwe Kruger +6
Surgical training in medical school residency programs has followed the apprenticeship model. The learning and assessment process is inherently subjective and time-consuming. Thus,…
OncoReg: Medical Image Registration for Oncological Challenges
Wiebke Heyer, Yannic Elser, Lennart Berkel +15
In modern cancer research, the vast volume of medical data generated is often underutilised due to challenges related to patient privacy. The OncoReg Challenge addresses this issue…
Explaining Chest X-ray Pathology Models using Textual Concepts
Vijay Sadashivaiah, Pingkun Yan, James A. Hendler
Deep learning models have revolutionized medical imaging and diagnostics, yet their opaque nature poses challenges for clinical adoption and trust. Amongst approaches to improve mo…
Transducer Adaptive Ultrasound Volume Reconstruction
Hengtao Guo, Sheng Xu, Bradford J. Wood +1
Reconstructed 3D ultrasound volume provides more context information compared to a sequence of 2D scanning frames, which is desirable for various clinical applications such as ultr…
Federated Cross Learning for Medical Image Segmentation
Xuanang Xu, Hannah H. Deng, Tianyi Chen +6
Federated learning (FL) can collaboratively train deep learning models using isolated patient data owned by different hospitals for various clinical applications, including medical…
Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning
Hengtao Guo, Sheng Xu, Bradford Wood +1
Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information. Current methods for 3D v…
Unified Multi-scale Feature Abstraction for Medical Image Segmentation
Xi Fang, Bo Du, Sheng Xu +2
Automatic medical image segmentation, an essential component of medical image analysis, plays an importantrole in computer-aided diagnosis. For example, locating and segmenting the…
Stabilizing Deep Tomographic Reconstruction
Weiwen Wu, Dianlin Hu, Wenxiang Cong +7
Tomographic image reconstruction with deep learning is an emerging field, but a recent landmark study reveals that several deep reconstruction networks are unstable for computed to…
Cross-modal Attention for MRI and Ultrasound Volume Registration
Xinrui Song, Hengtao Guo, Xuanang Xu +6
Prostate cancer biopsy benefits from accurate fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images. In the past few years, convolutional neural networks (CNNs…
General Purpose Image Encoder DINOv2 for Medical Image Registration
Xinrui Song, Xuanang Xu, Pingkun Yan
Existing medical image registration algorithms rely on either dataset specific training or local texture-based features to align images. The former cannot be reliably implemented w…
End-to-end Ultrasound Frame to Volume Registration
Hengtao Guo, Xuanang Xu, Sheng Xu +2
Fusing intra-operative 2D transrectal ultrasound (TRUS) image with pre-operative 3D magnetic resonance (MR) volume to guide prostate biopsy can significantly increase the yield. Ho…
X-ray Dissectography Improves Lung Nodule Detection
Chuang Niu, Giridhar Dasegowda, Pingkun Yan +2
Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often r…
Deep Learning-based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement
Xi Fang, Daeseung Kim, Xuanang Xu +8
Simulating facial appearance change following bony movement is a critical step in orthognathic surgical planning for patients with jaw deformities. Conventional biomechanics-based…
Robustified Domain Adaptation
Jiajin Zhang, Hanqing Chao, Pingkun Yan
Unsupervised domain adaptation (UDA) is widely used to transfer knowledge from a labeled source domain to an unlabeled target domain with different data distribution. While extensi…
Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports
Razi Mahmood, Diego Machado-Reyes, Joy Wu +7
With the emergence of large-scale vision language models (VLM), it is now possible to produce realistic-looking radiology reports for chest X-ray images. However, their clinical tr…
Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
Hanqing Chao, Hongming Shan, Fatemeh Homayounieh +7
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an op…
Evaluating Automated Radiology Report Quality through Fine-Grained Phrasal Grounding of Clinical Findings
Razi Mahmood, Pingkun Yan, Diego Machado Reyes +5
Several evaluation metrics have been developed recently to automatically assess the quality of generative AI reports for chest radiographs based only on textual information using l…
PINNOCHIO: Physics-Informed Neural Network for Coupled Hyperelastic Interface-Volume Simulation in Orthognathic Surgery
Jungwook Lee, Daeseung Kim, Kevin Gu +6
Predicting patient-specific facial soft-tissue deformation is critical for iterative orthognathic surgery planning. However, current computational methods face a strict accuracy-ef…
Evaluating Large Language Models for Radiology Natural Language Processing
Zhengliang Liu, Tianyang Zhong, Yiwei Li +43
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they hav…
Deep Compressive Macroscopic Fluorescence Lifetime Imaging
Ruoyang Yao, Marien Ochoa, Xavier Intes +1
Compressive Macroscopic Fluorescence Lifetime Imaging (MFLI) is a novel technical implementation that enables monitoring multiple molecular interactions in macroscopic scale. Espec…
Federated Multi-organ Segmentation with Inconsistent Labels
Xuanang Xu, Hannah H. Deng, Jaime Gateno +1
Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners, which helps address the concern of data pr…
Soft-tissue Driven Craniomaxillofacial Surgical Planning
Xi Fang, Daeseung Kim, Xuanang Xu +8
In CMF surgery, the planning of bony movement to achieve a desired facial outcome is a challenging task. Current bone driven approaches focus on normalizing the bone with the expec…
Noise Entangled GAN For Low-Dose CT Simulation
Chuang Niu, Ge Wang, Pingkun Yan +8
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image. First, we present two schemes to generate a clean CT…
Xray2Xray: World Model from Chest X-rays with Volumetric Context
Zefan Yang, Xinrui Song, Xuanang Xu +4
Chest X-rays (CXRs) are the most widely used medical imaging modality and play a pivotal role in diagnosing diseases. However, as 2D projection images, CXRs are limited by structur…
Multi-hop Convolutions on Weighted Graphs
Qikui Zhu, Bo Du, Pingkun Yan
Graph Convolutional Networks (GCNs) have made significant advances in semi-supervised learning, especially for classification tasks. However, existing GCN based methods have two ma…
Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba
Zefan Yang, Jiajin Zhang, Ge Wang +2
Accurate prediction of Cardiovascular disease (CVD) risk in medical imaging is central to effective patient health management. Previous studies have demonstrated that imaging featu…
AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray
Nkechinyere N. Agu, Joy T. Wu, Hanqing Chao +5
Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision. However, most existing deep learning models only look a…
Deeply-Supervised CNN for Prostate Segmentation
Qikui Zhu, Bo Du, Baris Turkbey +2
Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion. However, the lack of clear boundary specifically at the apex and b…
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar +4
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions. This paper considers a more realistic yet more challenging scenari…
Self-supervised Training of Graph Convolutional Networks
Qikui Zhu, Bo Du, Pingkun Yan
Graph Convolutional Networks (GCNs) have been successfully applied to analyze non-grid data, where the classical convolutional neural networks (CNNs) cannot be directly used. One s…
Learning Directional Semantic Transitions for Longitudinal Chest X-ray Analysis
Zhangfeng Hu, Zefan Yang, Ge Wang +4
Chest X-ray (CXR) interpretation often requires longitudinal comparison to assess disease progression. Existing approaches typically rely on temporal feature fusion or inter-study…
A Method of Rapid Quantification of Patient-Specific Organ Dose for CT Using Coupled Deep-Learning based Multi-Organ Segmentation and GPU-accelerated Monte Carlo Dose Computing
Zhao Peng, Xi Fang, Pingkun Yan +7
Purpose: This paper describes a new method to apply deep-learning algorithms for automatic segmentation of radiosensitive organs from 3D tomographic CT images before computing orga…
Fact-Checking of AI-Generated Reports
Razi Mahmood, Diego Machado Reyes, Ge Wang +2
With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for preliminary reads of radiology images. This can expe…
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar +4
Domain shift is a common problem in clinical applications, where the training images (source domain) and the test images (target domain) are under different distributions. Unsuperv…
Adversarial Image Registration with Application for MR and TRUS Image Fusion
Pingkun Yan, Sheng Xu, Ardeshir R. Rastinehad +1
Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications. For example, magnetic resonance…
Task-Oriented Low-Dose CT Image Denoising
Jiajin Zhang, Hanqing Chao, Xuanang Xu +3
The extensive use of medical CT has raised a public concern over the radiation dose to the patient. Reducing the radiation dose leads to increased CT image noise and artifacts, whi…
Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction
Xi Fang, Pingkun Yan
Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-org…
Distance Map Supervised Landmark Localization for MR-TRUS Registration
Xinrui Song, Xuanang Xu, Sheng Xu +4
In this work, we propose to explicitly use the landmarks of prostate to guide the MR-TRUS image registration. We first train a deep neural network to automatically localize a set o…
Deep Learning in Medical Image Registration: A Survey
Grant Haskins, Uwe Kruger, Pingkun Yan
The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitori…
Knowledge-based Analysis for Mortality Prediction from CT Images
Hengtao Guo, Uwe Kruger, Ge Wang +2
Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality. Low-Do…
Decreasing the Surgical Errors by Neurostimulation of Primary Motor Cortex and the Associated Brain Activation via Neuroimaging
Yuanyuan Gao, Lora Cavuoto, Anirban Dutta +9
Acquisition of fine motor skills is a time-consuming process as it requires frequent repetitions. Transcranial electrical stimulation is a promising means of enhancing simple motor…
Regression Metric Loss: Learning a Semantic Representation Space for Medical Images
Hanqing Chao, Jiajin Zhang, Pingkun Yan
Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores. While training strategies and loss functions hav…
Disease-informed Adaptation of Vision-Language Models
Jiajin Zhang, Ge Wang, Mannudeep K. Kalra +1
In medical image analysis, the expertise scarcity and the high cost of data annotation limits the development of large artificial intelligence models. This paper investigates the p…
Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation
Qikui Zhu, Bo Du, Pingkun Yan
Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segme…
CT Image Denoising with Perceptive Deep Neural Networks
Qingsong Yang, Pingkun Yan, Mannudeep K. Kalra +1
Increasing use of CT in modern medical practice has raised concerns over associated radiation dose. Reduction of radiation dose associated with CT can increase noise and artifacts,…
OASIS: One-pass aligned Atlas Set for Image Segmentation
Qikui Zhu, Bo Du, Pingkun Yan
Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task…
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation
Xueying Chen, Rong Zhang, Pingkun Yan
Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently, deep learning based image segmentation methods have achieved promising performance,…
On a Sparse Shortcut Topology of Artificial Neural Networks
Fenglei Fan, Dayang Wang, Hengtao Guo +4
In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effe…
Hybrid deep neural networks for all-cause Mortality Prediction from LDCT Images
Pingkun Yan, Hengtao Guo, Ge Wang +2
Known for its high morbidity and mortality rates, lung cancer poses a significant threat to human health and well-being. However, the same population is also at high risk for other…
Multifactorial cancer treatment outcome prediction through multifaceted radiomics
Zhiguo Zhou, David Sher, Qiongwen Zhang +6
Accurately predicting the treatment outcome plays a greatly important role in tailoring and adapting a treatment planning in cancer therapy. Although the development of different m…
Integrative Analysis for COVID-19 Patient Outcome Prediction
Hanqing Chao, Xi Fang, Jiajin Zhang +13
While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome predictio…
Specialty-Oriented Generalist Medical AI for Chest CT Screening
Chuang Niu, Qing Lyu, Christopher D. Carothers +6
Modern medical records include a vast amount of multimodal free text clinical data and imaging data from radiology, cardiology, and digital pathology. Fully mining such big data re…
Integrating AI in College Education: Positive yet Mixed Experiences with ChatGPT
Xinrui Song, Jiajin Zhang, Pingkun Yan +4
The integration of artificial intelligence (AI) chatbots into higher education marks a shift towards a new generation of pedagogical tools, mirroring the arrival of milestones like…