134 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of Texas Southwestern Medical CenterUS50 papers
- Advanced Imaging Research (United States)US7 papers
- Stanford UniversityUS5 papers
- Massachusetts General HospitalUS4 papers
- Harvard UniversityUS3 papers
- Harvard University PressUS3 papers
- Johns Hopkins UniversityUS3 papers
- National Institutes of HealthUS3 papers
- Radiation Oncology AssociatesUS3 papers
- The University of Texas at DallasUS3 papers
- The University of Texas MD Anderson Cancer CenterUS3 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS2 papers
50 papers
Reviewing Model Collapse and Countermeasures
Xihao Xie, Beichen Hu
Driven by massive amounts of web-scale data, generative AI (GenAI) has achieved remarkable progress, enabling various applications in diverse sectors. The advances of GenAI have ac…
Scale-aware Adaptive Supervised Network with Limited Medical Annotations
Zihan Li, Dandan Shan, Yunxiang Li +2
Medical image segmentation faces critical challenges in semi-supervised learning scenarios due to severe annotation scarcity requiring expert radiological knowledge, significant in…
Quantum Entanglement Control in Two-Spin-1/2 NMR Systems Through Magnetic Fields and Temperature
Fatemeh Khashami, Stefan Glöggler
We investigate quantum entanglement in two-spin-1/2 NMR systems at thermal equilibrium under external magnetic fields. We derive closed-form analytical expressions for the entangle…
MC BTS: simultaneously resolving magnetization transfer effect and relaxation for multiple components
Albert Jang, Hyungseok Jang, Nian Wang +2
We propose a signal acquisition and modeling framework for multi-component tissue quantification that encompasses transmit field inhomogeneity, multi-component relaxation and magne…
Deformable Image Registration for Self-supervised Cardiac Phase Detection in Multi-View Multi-Disease Cardiac Magnetic Resonance Images
Sven Koehler, Sarah Kaye Mueller, Jonathan Kiekenap +6
Cardiovascular magnetic resonance (CMR) is the gold standard for assessing cardiac function, but individual cardiac cycles complicate automatic temporal comparison or sub-phase ana…
Causal Machine Learning for Surgical Interventions
J. Ben Tamo, Nishant S. Chouhan, Micky C. Nnamdi +6
Surgical decision-making is complex and requires understanding causal relationships between patient characteristics, interventions, and outcomes. In high-stakes settings like spina…
Advancing Problem-Based Learning in Biomedical Engineering in the Era of Generative AI
Micky C. Nnamdi, J. Ben Tamo, Benoit Marteau +2
Problem-Based Learning (PBL) has significantly impacted biomedical engineering (BME) education since its introduction in the early 2000s, effectively enhancing critical thinking an…
A Conditional Point Cloud Diffusion Model for Deformable Liver Motion Tracking Via a Single Arbitrarily-Angled X-ray Projection
Jiacheng Xie, Hua-Chieh Shao, Yunxiang Li +4
Deformable liver motion tracking using a single X-ray projection enables real-time motion monitoring and treatment intervention. We introduce a conditional point cloud diffusion mo…
The Longitudinal Health, Income, and Employment Model (LHIEM): a discrete-time microsimulation model for policy analysis
Adrienne M. Propp, Raffaele Vardavas, Carter C. Price +1
Dynamic microsimulation has long been recognized as a powerful tool for policy analysis, but in fact most major health policy simulations lack path dependency, a critical feature f…
Irreversibility in Bacterial Regulatory Networks
Yi Zhao, Thomas P. Wytock, Kimberly A. Reynolds +1
Irreversibility, in which a transient perturbation leaves a system in a new state, is an emergent property in systems of interacting entities. This property has well-established im…
TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy
Meixu Chen, Kai Wang, Michael Dohopolski +3
Early identification of head and neck cancer (HNC) patients who would experience significant anatomical change during radiotherapy (RT) is important to optimize patient clinical be…
Enabling Global Image Data Sharing in the Life Sciences
Peter Bajcsy, Sreenivas Bhattiprolu, Katy Boerner +20
Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we tak…
Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine
Qiao Jin, Fangyuan Chen, Yiliang Zhou +15
Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily…
Improved test-retest reliability of and susceptibility quantification using multi-shot multi echo 3D EPI
Yujia Huang, Lin Chen, Xu Li +1
This study aimed to evaluate the potential of 3D echo-planar imaging (EPI) for improving the reliability of -weighted () data and quantification of …
Dirichlet Diffusion Score Model for Biological Sequence Generation
Pavel Avdeyev, Chenlai Shi, Yuhao Tan +2
Designing biological sequences is an important challenge that requires satisfying complex constraints and thus is a natural problem to address with deep generative modeling. Diffus…
Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
Yunxiang Li, Hua-Chieh Shao, Xiao Liang +5
Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing di…
Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Michael Dohopolski, Kai Wang, Biling Wang +5
Prediction uncertainty estimation has clinical significance as it can potentially quantify prediction reliability. Clinicians may trust 'blackbox' models more if robust reliability…
Dynamic Cone-beam CT Reconstruction using Spatial and Temporal Implicit Neural Representation Learning (STINR)
You Zhang, Tielige Mengke
Objective: Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable…
Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option
Kai Wang, Michael Dohopolski, Qiongwen Zhang +2
A reliable locoregional recurrence (LRR) prediction model is important for the personalized management of head and neck cancers (HNC) patients. This work aims to develop a delta-ra…
Ring deconvolution microscopy: exploiting symmetry for efficient spatially varying aberration correction
Amit Kohli, Anastasios N. Angelopoulos, David McAllister +7
The most ubiquitous form of computational aberration correction for microscopy is deconvolution. However, deconvolution relies on the assumption that the point spread function is t…
Deep Learning based Direct Segmentation Assisted by Deformable Image Registration for Cone-Beam CT based Auto-Segmentation for Adaptive Radiotherapy
Xiao Liang, Howard Morgan, Ti Bai +3
Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians to edit contours. However, deep learning (DL)-bas…
Leveraging Global Binary Masks for Structure Segmentation in Medical Images
Mahdieh Kazemimoghadam, Zi Yang, Lin Ma +3
Deep learning (DL) models for medical image segmentation are highly influenced by intensity variations of input images and lack generalization due to primarily utilizing pixels' in…
Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI
Xirui Hou, Pengfei Guo, Puyang Wang +18
Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging met…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…