1.7k citations · 2.9k across the 20 of their papers we have counts for
21 papers
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…
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…
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…
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…
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…
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…
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…
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…
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…
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,…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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,…