activity
20142024
most citedDirect Estimation of Spinal Cobb Angles by Structured Multi-Output Regression

105 citations · 110 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CV20241 cited

Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts

Qin Liu, Jaemin Cho, Mohit Bansal +1

The goal of interactive image segmentation is to delineate specific regions within an image via visual or language prompts. Low-latency and high-quality interactive segmentation wi…

cs.CV2024

A Unified Model for Longitudinal Multi-Modal Multi-View Prediction with Missingness

Boqi Chen, Junier Oliva, Marc Niethammer

Medical records often consist of different modalities, such as images, text, and tabular information. Integrating all modalities offers a holistic view of a patient's condition, wh…

eess.IV2024

NeuralOCT: Airway OCT Analysis via Neural Fields

Yining Jiao, Amy Oldenburg, Yinghan Xu +4

Optical coherence tomography (OCT) is a popular modality in ophthalmology and is also used intravascularly. Our interest in this work is OCT in the context of airway abnormalities…

cs.CV2024

uniGradICON: A Foundation Model for Medical Image Registration

Lin Tian, Hastings Greer, Roland Kwitt +5

Conventional medical image registration approaches directly optimize over the parameters of a transformation model. These approaches have been highly successful and are used generi…

cs.CV20231 cited

Joint Depth Prediction and Semantic Segmentation with Multi-View SAM

Mykhailo Shvets, Dongxu Zhao, Marc Niethammer +2

Multi-task approaches to joint depth and segmentation prediction are well-studied for monocular images. Yet, predictions from a single-view are inherently limited, while multiple v…

cs.CV2023

Self-supervised Landmark Learning with Deformation Reconstruction and Cross-subject Consistency Objectives

Chun-Hung Chao, Marc Niethammer

A Point Distribution Model (PDM) is the basis of a Statistical Shape Model (SSM) that relies on a set of landmark points to represent a shape and characterize the shape variation.…