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Found 6 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.
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
We introduce Llama Guard, an LLM-based input-output safeguard model geared towards Human-AI conversation use cases.
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images
However, these augmentation methods focus on shape augmentation, whereas deep learning models exhibit image-based texture bias resulting in sub-optimal models.
Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy
Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population.
Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries
This paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that capture morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population.
RENs: Relevance Encoding Networks
With extensive experimentation on synthetic and public image datasets, we show that the proposed model learns the relevant latent bottleneck dimensionality without compromising the representation and generation quality of the samples.
