Search Results for author:
Found 16 papers, 2 papers with code
SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images
Traditional methods for shape modeling from imaging data demand significant manual and computational resources.
Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation
This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of available un-annotated data.
StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining
Hematoxylin and Eosin (H&E) staining is the most commonly used for disease diagnosis and tumor recurrence tracking.
EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration
Secondly, feature similarities across attention heads that were recently found in multi-head attention architectures indicate a significant computational redundancy, suggesting that the capacity of the network could be better utilized to enhance performance.
Ranked #1 on
Medical Image Registration
on OASIS
(val dsc metric)
MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images
Statistical Shape Modeling (SSM) is an effective method for quantitatively analyzing anatomical variations within populations.
Particle-Based Shape Modeling for Arbitrary Regions-of-Interest
We propose an extension to \particle-based shape modeling (PSM), a widely used SSM framework, to allow shape modeling to arbitrary regions of interest.
Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon
Quantitative metrics such as FID and SSIM are frequently used for the analysis of generative models, but they do not correlate explicitly with higher-quality virtual staining results.
Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation
Deep learning based methods for automatic organ segmentation have shown promise in aiding diagnosis and treatment planning.
Image2SSM: Reimagining Statistical Shape Models from Images with Radial Basis Functions
This RBF-based shape representation offers a rich self-supervised signal for the network to estimate a continuous, yet compact representation of the underlying surface that can adapt to complex geometries in a data-driven manner.
Learning Deep Features for Shape Correspondence with Domain Invariance
Further, unsupervised learning is demonstrated to learn complex anatomy features using the supervised domain adaptation from features learned on simpler anatomy.
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening
The imaging process required for 3D reconstructions involves a highly iterative and empirical screening process, starting with the acquisition of low magnification images of the cryo-EM grids.
A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration
We propose a novel neural network architecture that simultaneously learns and uses the population-level statistics of the spatial transformations to regularize the neural networks for unsupervised image registration.
DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images
Statistical shape modeling is an important tool to characterize variation in anatomical morphology.
Clustering With Pairwise Relationships: A Generative Approach
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limited by logistics and expense.
Analytic Bilinear Appearance Subspace Construction for Modeling Image Irradiance under Natural Illumination and Non-Lambertian Reflectance
Conventional subspace construction approaches suffer from the need of "large-enough" image ensemble rendering numerical methods intractable.
