86 citations · 267 across the 20 of their papers we have counts for
20 papers
End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT
Yoseob Han, Dufan Wu, Kyungsang Kim +1
Objective: There exist several X-ray computed tomography (CT) scanning strategies to reduce a radiation dose, such as (1) sparse-view CT, (2) low-dose CT, and (3) region-of-interes…
An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models
Yuang Wang, Pengfei Jin, Li Zhang +3
Diffusion bridge models have demonstrated promising performance in conditional image generation tasks, such as image restoration and translation, by initializing the generative pro…
Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising
Siyeop Yoon, Rui Hu, Yuang Wang +6
PET imaging is a powerful modality offering quantitative assessments of molecular and physiological processes. The necessity for PET denoising arises from the intrinsic high noise…
Hallucination Index: An Image Quality Metric for Generative Reconstruction Models
Matthew Tivnan, Siyeop Yoon, Zhennong Chen +3
Generative image reconstruction algorithms such as measurement conditioned diffusion models are increasingly popular in the field of medical imaging. These powerful models can tran…
Measurement Embedded Schrödinger Bridge for Inverse Problems
Yuang Wang, Pengfei Jin, Siyeop Yoon +4
Score-based diffusion models are frequently employed as structural priors in inverse problems. However, their iterative denoising process, initiated from Gaussian noise, often resu…
Contrastive Learning Via Equivariant Representation
Sifan Song, Jinfeng Wang, Qiaochu Zhao +6
Invariant Contrastive Learning (ICL) methods have achieved impressive performance across various domains. However, the absence of latent space representation for distortion (augmen…
Implicit Image-to-Image Schrodinger Bridge for Image Restoration
Yuang Wang, Siyeop Yoon, Pengfei Jin +8
Diffusion-based models have demonstrated remarkable effectiveness in image restoration tasks; however, their iterative denoising process, which starts from Gaussian noise, often le…
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
Cheng Chen, Juzheng Miao, Dufan Wu +10
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation ta…
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study
Zihan Guan, Zihao Wu, Zhengliang Liu +5
Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in c…
Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer
Yutong Xie, Dufan Wu, Bin Dong +1
We proved that a trained model in supervised deep learning minimizes the conditional risk for each input (Theorem 2.1). This property provided insights into the behavior of trained…
Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection
Varun Buch, Aoxiao Zhong, Xiang Li +8
COVID-19 patient triaging with predictive outcome of the patients upon first present to emergency department (ED) is crucial for improving patient prognosis, as well as better hosp…
Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19
Aoxiao Zhong, Xiang Li, Dufan Wu +17
In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public…
Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis
Young-Gon Kim, Kyungsang Kim, Dufan Wu +10
Purpose. Imaging plays an important role in assessing severity of COVID 19 pneumonia. However, semantic interpretation of chest radiography (CXR) findings does not include quantita…
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging
Ziju Shen, Yufei Wang, Dufan Wu +2
Computed Tomography (CT) takes X-ray measurements on the subjects to reconstruct tomographic images. As X-ray is radioactive, it is desirable to control the total amount of dose of…
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks
Dufan Wu, Hui Ren, Quanzheng Li
Dynamic computed tomography perfusion (CTP) imaging is a promising approach for acute ischemic stroke diagnosis and evaluation. Hemodynamic parametric maps of cerebral parenchyma a…
Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography
Dufan Wu, Daniel Montes, Ziheng Duan +4
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of…
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples
Dufan Wu, Kuang Gong, Kyungsang Kim +1
Deep neural networks have been proved efficient for medical image denoising. Current training methods require both noisy and clean images. However, clean images cannot be acquired…
Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction
Dufan Wu, Kyungsang Kim, Quanzheng Li
Deep-neural-network-based image reconstruction has demonstrated promising performance in medical imaging for under-sampled and low-dose scenarios. However, it requires large amount…
End-to-end Lung Nodule Detection in Computed Tomography
Dufan Wu, Kyungsang Kim, Bin Dong +2
Computer aided diagnostic (CAD) system is crucial for modern med-ical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Co…
A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising
Dufan Wu, Kyungsang Kim, Georges El Fakhri +1
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods…