47 papers
This study demonstrates that enhancing morpho-kinematic analysis of leaf movements through non-linear descriptors, irrigation-context variables, and adaptive ensemble methods significantly improves the robustness and accuracy of low-cost, RGB-based plant water stress classification.
Spinverse is a differentiable physics framework that reconstructs explicit microstructural interfaces from diffusion MRI by optimizing learnable face permeabilities on a fixed tetrahedral grid, utilizing geometric priors and multi-sequence optimization to overcome ill-posedness and recover complex tissue geometries without altering mesh connectivity.
This paper introduces AbAffinity, a large language model designed to accurately predict the binding affinity of antibodies against target peptides, such as the SARS-CoV-2 spike protein, to advance machine learning-based antibody design.
This study presents an AI implementation science case study at Shriners Childrens that modernizes its research data infrastructure to the OMOP CDM standard, introduces a Python-based tool extending data quality assessment with Trustworthy AI principles, and evaluates hybrid implementation strategies for clinical applications like Craniofacial Microsomia to accelerate trustworthy AI adoption in healthcare.
This paper introduces a gradient-based optimization method using straight-through Gumbel-Softmax estimation to enable efficient parameter inference and inverse design in exact stochastic kinetic models by approximating gradients through continuous relaxation while preserving discrete stochastic dynamics in the forward pass.
This paper introduces SMMA, a fully automated deep learning framework that accurately measures geniohyoid muscle thickness during speech, enabling scalable analysis of speech motor control and objective assessment of related disorders by eliminating the need for time-consuming manual annotation.
This paper introduces ERDES, the first open-access benchmark dataset of ocular ultrasound videos labeled for retinal detachment presence and macular status, alongside baseline performance evaluations of 40 deep learning models across eight architectures to advance automated diagnosis in resource-limited settings.