51 citations · 58 across the 12 of their papers we have counts for
12 papers
VLM- and LLM-Driven Multi-Agent System for PET Image Denoising
Boxiao Yu, Savas Ozdemir, Yang Xing +8
Positron emission tomography (PET) imaging suffers from limited spatial resolution and low signal-to-noise ratio, which can compromise quantitative accuracy and lesion detectabilit…
MedVL-SAM2: A unified 3D medical vision-language model for multimodal reasoning and prompt-driven segmentation
Yang Xing, Jiong Wu, Savas Ozdemir +4
Recent progress in medical vision-language models (VLMs) has achieved strong performance on image-level text-centric tasks such as report generation and visual question answering (…
SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting
Yang Xing, Jiong Wu, Yuheng Bu +1
Although new vision foundation models such as Segment Anything Model 2 (SAM2) have significantly enhanced zero-shot image segmentation capabilities, reliance on human-provided prom…
CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation
Jiong Wu, Yang Xing, Boxiao Yu +2
Most publicly available medical segmentation datasets are only partially labeled, with annotations provided for a subset of anatomical structures. When multiple datasets are combin…
PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts
Boxiao Yu, Savas Ozdemir, Jiong Wu +4
Low-dose Positron Emission Tomography (PET) imaging presents a significant challenge due to increased noise and reduced image quality, which can compromise its diagnostic accuracy…
LDM-Morph: Latent diffusion model guided deformable image registration
Jiong Wu, Kuang Gong
Deformable image registration plays an essential role in various medical image tasks. Existing deep learning-based deformable registration frameworks primarily utilize convolutiona…
Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
Jiong Wu, Shuang Zhou, Li Lin +2
Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topolog…
HNAS-reg: hierarchical neural architecture search for deformable medical image registration
Jiong Wu, Yong Fan
Convolutional neural networks (CNNs) have been widely used to build deep learning models for medical image registration, but manually designed network architectures are not necessa…
BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images
Li Lin, Zhonghua Wang, Jiewei Wu +5
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging technique that allows visualizations of vasculature and foveal avascular zone (FAZ) across retinal l…
Coarse-to-fine Kidney Segmentation Framework Incorporating with Abnormal Detection and Correction
Yue Zhang, Jiong Wu, Yu Zhou +2
In this paper, we formulated the kidney segmentation task in a coarse-to-fine fashion, predicting a coarse label based on the entire CT image and a fine label based on the coarse s…
Prostate segmentation using Z-net
Yue Zhang, Jiong Wu, Wanli Chen +2
In this paper, we proposed a novel architecture of convolutional neural network (CNN), namely Z-net, for segmenting prostate from magnetic resonance images (MRIs). In the proposed…
Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles
Jiong Wu, Xiaoying Tang
In this study, we proposed and validated a multi-atlas guided 3D fully convolutional network (FCN) ensemble model (M-FCN) for segmenting brain regions of interest (ROIs) from struc…