141 citations · 200 across the 20 of their papers we have counts for
6 papers · 1 filter
Leveraging sinusoidal representation networks to predict fMRI signals from EEG
Yamin Li, Ange Lou, Ziyuan Xu +2
In modern neuroscience, functional magnetic resonance imaging (fMRI) has been a crucial and irreplaceable tool that provides a non-invasive window into the dynamics of whole-brain…
False Negative/Positive Control for SAM on Noisy Medical Images
Xing Yao, Han Liu, Dewei Hu +9
The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixe…
WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters
Ange Lou, Jack Noble
Depth estimation in surgical video plays a crucial role in many image-guided surgery procedures. However, it is difficult and time consuming to create depth map ground truth datase…
SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)
Ange Lou, Yamin Li, Xing Yao +2
The accurate reconstruction of surgical scenes from surgical videos is critical for various applications, including intraoperative navigation and image-guided robotic surgery autom…
Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025
Aneeq Zia, Max Berniker, Rogerio Garcia Nespolo +153
Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms…
CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects
Ange Lou, Shuyue Guan, Murray Loew
Segmenting medical images accurately and reliably is important for disease diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes, shapes,…