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Found 17 papers, 8 papers with code
ACE: A fast, skillful learned global atmospheric model for climate prediction
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation
Then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks.
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation
In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation.
Deep Learning for Breast MRI Style Transfer with Limited Training Data
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data.
Generative Modeling of High-resolution Global Precipitation Forecasts
Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change.
A Mixing Time Lower Bound for a Simplified Version of BART
In this paper, we provide the first lower bound on the mixing time for a simplified version of BART in which we reduce the sum to a single tree and use a subset of the possible moves for the MCMC proposal distribution.
Learning correspondences of cardiac motion from images using biomechanics-informed modeling
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures.
Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness
Magnetic Resonance Spectroscopic Imaging (MRSI) is a valuable tool for studying metabolic activities in the human body, but the current applications are limited to low spatial resolutions.
Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data
Machine learning in high-stakes domains, such as healthcare, faces two critical challenges: (1) generalizing to diverse data distributions given limited training data while (2) maintaining interpretability.
Surrogate Gap Minimization Improves Sharpness-Aware Training
Instead, we define a \textit{surrogate gap}, a measure equivalent to the dominant eigenvalue of Hessian at a local minimum when the radius of the neighborhood (to derive the perturbed loss) is small.
Fast Interpretable Greedy-Tree Sums
In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure.
Multiple-shooting adjoint method for whole-brain dynamic causal modeling
Furthermore, MSA uses the adjoint method for accurate gradient estimation in the ODE; since the adjoint method is generic, MSA is a generic method for both linear and non-linear systems, and does not require re-derivation of the algorithm as in EM.
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Neural ordinary differential equations (NODEs) have recently attracted increasing attention; however, their empirical performance on benchmark tasks (e. g. image classification) are significantly inferior to discrete-layer models.
Curating a COVID-19 data repository and forecasting county-level death counts in the United States
We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead.
Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data.
Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging
Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available.
