8 papers
ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation
Yu Xin, Gorkem Can Ates, Jun Ma +5
Text guided 3D medical image segmentation offers a flexible alternative to class based and spatial prompt based models by allowing users to specify regions of interest directly in…
Structure-Adaptive Sparse Diffusion in Voxel Space for 3D Medical Image Enhancement
Hongxu Jiang, Fei Li, Boxiao Yu +4
Three-dimensional (3D) medical image enhancement, including denoising and super-resolution, is critical for clinical diagnosis in CT, PET, and MRI. Although diffusion models have s…
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 (…
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis
Yu Xin, Gorkem Can Ates, Kuang Gong +1
Vision-language models (VLMs) have shown promise in 2D medical image analysis, but extending them to 3D remains challenging due to the high computational demands of volumetric data…
Geodesic Diffusion Models for Efficient Medical Image Enhancement
Teng Zhang, Hongxu Jiang, Kuang Gong +1
Diffusion models generate data by learning to reverse a forward process, where samples are progressively perturbed with Gaussian noise according to a predefined noise schedule. Fro…
TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models
Yuxin Gong, Se-in Jang, Wei Shao +2
Accurate quantification of tau pathology via tau positron emission tomography (PET) scan is crucial for diagnosing and monitoring Alzheimer's disease (AD). However, the high cost a…