36 citations · 125 across the 21 of their papers we have counts for
22 papers
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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,…
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…
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…
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…
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…
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…
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…
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…
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…
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…