36 citations · 73 across the 14 of their papers we have counts for
14 papers
Improving Medical Image Generative Models with Fréchet Distance Loss
Andrew Marshall, Xuanang Xu, Xiaoran Zhang +3
Diffusion generative models have demonstrated immense potential for synthetic medical image generation. However, these models often struggle to capture complex morphological charac…
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…
OncoReg: Medical Image Registration for Oncological Challenges
Wiebke Heyer, Yannic Elser, Lennart Berkel +16
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…
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…
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…
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-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…
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…
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Aaron Babier, Rafid Mahmood, Binghao Zhang +56
We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients 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…
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…
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…