260 citations · 1.5k across the 65 of their papers we have counts for
65 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…
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…
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data
Hongmin Cai, Xiaoke Huang, Zhengliang Liu +8
Alzheimer's disease (AD) is a common form of dementia that severely impacts patient health. As AD impairs the patient's language understanding and expression ability, the speech of…
Segment Anything Model (SAM) for Radiation Oncology
Lian Zhang, Zhengliang Liu, Lu Zhang +10
In this study, we evaluate the performance of the Segment Anything Model (SAM) in clinical radiotherapy. Our results indicate that SAM's 'segment anything' mode can achieve clinica…
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Haixing Dai, Mengxuan Hu, Qing Li +12
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The…
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…
SAMAug: Point Prompt Augmentation for Segment Anything Model
Haixing Dai, Chong Ma, Zhiling Yan +14
This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generat…
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
Haixing Dai, Yiwei Li, Zhengliang Liu +14
In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which…
Radiology-GPT: A Large Language Model for Radiology
Zhengliang Liu, Aoxiao Zhong, Yiwei Li +17
We introduce Radiology-GPT, a large language model for radiology. Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstr…
Artificial General Intelligence for Medical Imaging Analysis
Xiang Li, Lin Zhao, Lu Zhang +16
Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general…
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
Kai Zhang, Rong Zhou, Eashan Adhikarla +20
Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to ut…
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
Zihao Wu, Lu Zhang, Chao Cao +12
Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capabili…
Unsupervised Image Denoising with Score Function
Yutong Xie, Mingze Yuan, Bin Dong +1
Though achieving excellent performance in some cases, current unsupervised learning methods for single image denoising usually have constraints in applications. In this paper, we p…
AugGPT: Leveraging ChatGPT for Text Data Augmentation
Haixing Dai, Zhengliang Liu, Wenxiong Liao +15
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially p…
Decentralized Riemannian natural gradient methods with Kronecker-product approximations
Jiang Hu, Kangkang Deng, Na Li +1
With a computationally efficient approximation of the second-order information, natural gradient methods have been successful in solving large-scale structured optimization problem…
DULDA: Dual-domain Unsupervised Learned Descent Algorithm for PET image reconstruction
Rui Hu, Yunmei Chen, Kyungsang Kim +3
Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heav…
A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity
Jiang Hu, Kangkang Deng, Jiayuan Wu +1
This paper aims to develop a Newton-type method to solve a class of nonconvex composite programs. In particular, the nonsmooth part is possibly nonconvex. To tackle the nonconvexit…
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4
Zhengliang Liu, Yue Huang, Xiaowei Yu +15
The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Port…
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images
Gary Y. Li, Junyu Chen, Se-In Jang +2
Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite…
Diffusion Model for Generative Image Denoising
Yutong Xie, Minne Yuan, Bin Dong +1
In supervised learning for image denoising, usually the paired clean images and noisy images are collected or synthesised to train a denoising model. L2 norm loss or other distance…
Investigation of Network Architecture for Multimodal Head-and-Neck Tumor Segmentation
Ye Li, Junyu Chen, Se-in Jang +2
Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have fl…
Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)
Arman Rahmim, Tyler J. Bradshaw, Irène Buvat +10
The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community me…
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
Hongmin Cai, Wenxiong Liao, Zhengliang Liu +12
Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming an…
PET image denoising based on denoising diffusion probabilistic models
Kuang Gong, Keith A. Johnson, Georges El Fakhri +2
Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic models (DDPM) are distr…