11 citations · 24 across the 12 of their papers we have counts for
12 papers
Adapting Segment Anything Model 3 for Concept-Driven Lesion Segmentation in Medical Images: An Experimental Study
Guoping Xu, Jayaram K. Udupa, Yubing Tong +5
Accurate lesion segmentation is essential in medical image analysis, yet most existing methods are designed for specific anatomical sites or imaging modalities, limiting their gene…
Exploiting DINOv3-Based Self-Supervised Features for Robust Few-Shot Medical Image Segmentation
Guoping Xu, Jayaram K. Udupa, Weiguo Lu +1
Deep learning-based automatic medical image segmentation plays a critical role in clinical diagnosis and treatment planning but remains challenging in few-shot scenarios due to the…
Is the medical image segmentation problem solved? A survey of current developments and future directions
Guoping Xu, Jayaram K. Udupa, Jax Luo +8
Medical image segmentation has advanced rapidly over the past two decades, largely driven by deep learning, which has enabled accurate and efficient delineation of cells, tissues,…
Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future
Guoping Xu, Jayaram K. Udupa, Yajun Yu +4
Video Object Segmentation and Tracking (VOST) presents a complex yet critical challenge in computer vision, requiring robust integration of segmentation and tracking across tempora…
Predicting Risk of Pulmonary Fibrosis Formation in PASC Patients
Wanying Dou, Gorkem Durak, Koushik Biswas +14
While the acute phase of the COVID-19 pandemic has subsided, its long-term effects persist through Post-Acute Sequelae of COVID-19 (PASC), commonly known as Long COVID. There remai…
Eyes Tell the Truth: GazeVal Highlights Shortcomings of Generative AI in Medical Imaging
David Wong, Bin Wang, Gorkem Durak +23
The demand for high-quality synthetic data for model training and augmentation has never been greater in medical imaging. However, current evaluations predominantly rely on computa…
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…
XctDiff: Reconstruction of CT Images with Consistent Anatomical Structures from a Single Radiographic Projection Image
Qingze Bai, Tiange Liu, Zhi Liu +3
In this paper, we present XctDiff, an algorithm framework for reconstructing CT from a single radiograph, which decomposes the reconstruction process into two easily controllable t…
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…
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Awais Mansoor, Ulas Bagci, Brent Foster +5
Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide…
Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images
Ulas Bagci, Jayaram K. Udupa, Xinjian Chen
This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searc…
The Influence of Intensity Standardization on Medical Image Registration
Ulas Bagci, Jayaram K. Udupa, Li Bai
Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intens…