40 citations · 131 across the 30 of their papers we have counts for
16 papers · 1 filter
NV-Generate-Body: 3D Latent Diffusion Model forControllable Anatomy, Tumor, and Demographic Body Mask Synthesis
Can Zhao, Pengfei Guo, Dong Yang +8
Curating large labeled CT datasets is expensive and constrained by patient privacy. We present NV-Generate-BodyMask, a 3D latent diffusion model that synthesizes whole-body CT segm…
NV-Reason-CT: 3D Visual Language Model for CT Analysis
Andriy Myronenko, Dong Yang, Yucheng Tang +15
We present NV-Reason-CT, a generative vision--language model for chest and abdominal CT combining native 3D visual encoding with radiologist-guided reasoning. The model couples a n…
Align then Refine: Text-Guided 3D Prostate Lesion Segmentation
Cuiling Sun, Linkai Peng, Adam Murphy +9
Automated 3D segmentation of prostate lesions from biparametric MRI (bp-MRI) is essential for reliable algorithmic analysis, but achieving high precision remains challenging. Volum…
Reasoning Visual Language Model for Chest X-Ray Analysis
Andriy Myronenko, Dong Yang, Baris Turkbey +10
Vision-language models (VLMs) have shown strong promise for medical image analysis, but most remain opaque, offering predictions without the transparent, stepwise reasoning clinici…
MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss
Can Zhao, Pengfei Guo, Dong Yang +7
Medical image synthesis is an important topic for both clinical and research applications. Recently, diffusion models have become a leading approach in this area. Despite their str…
VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge
Vishwesh Nath, Wenqi Li, Dong Yang +22
Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is esse…
Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection
Alex Chen, Nathan Lay, Stephanie Harmon +6
Prostate cancer is one of the most prevalent malignancies in the world. While deep learning has potential to further improve computer-aided prostate cancer detection on MRI, its ef…
VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging
Yufan He, Pengfei Guo, Yucheng Tang +11
Foundation models for interactive segmentation in 2D natural images and videos have sparked significant interest in building 3D foundation models for medical imaging. However, the…
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
Bin Wang, Hongyi Pan, Armstrong Aboah +7
Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as…
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…
Cross-modal Attention for MRI and Ultrasound Volume Registration
Xinrui Song, Hengtao Guo, Xuanang Xu +6
Prostate cancer biopsy benefits from accurate fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images. In the past few years, convolutional neural networks (CNNs…
Multi-Domain Image Completion for Random Missing Input Data
Liyue Shen, Wentao Zhu, Xiaosong Wang +9
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-par…
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
Ling Zhang, Xiaosong Wang, Dong Yang +7
Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, in clinically realistic environments, such methods have marginal perform…
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
Naji Khosravan, Haydar Celik, Baris Turkbey +3
There are at least two categories of errors in radiology screening that can lead to suboptimal diagnostic decisions and interventions:(i)human fallibility and (ii)complexity of vis…
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…
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation
Naji Khosravan, Haydar Celik, Baris Turkbey +9
This study introduced a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists' reading experience with an automatic image…