7 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
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…
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
Hongxu Jiang, Muhammad Imran, Teng Zhang +4
Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented success in computer vision. However, they remain underutilized in medical imaging, a field crucial for…
A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention
Amarjeet Kumar, Hongxu Jiang, Muhammad Imran +7
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high co…