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Found 14 papers, 6 papers with code
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation
With retrieval-augmented interpretation, EchoPrime integrates information from all echocardiogram videos in a comprehensive study and performs holistic comprehensive clinical echocardiography interpretation.
SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals
This work demonstrates the value of holistic multi-modal sleep modeling to fully capture the richness of sleep recordings.
Deep Learning Discovery of Demographic Biomarkers in Echocardiography
When predicting race, we show that tuning the proportion of a confounding variable (sex) in the training data significantly impacts model AUC (ranging from 0. 57 to 0. 84), while in training a sex prediction model, tuning a confounder (race) did not substantially change AUC (0. 81 - 0. 83).
Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality
The algorithm discriminates mortality with an AUC of 0. 83 (95% CI 0. 79-0. 87) surpassing the discrimination of the RCRI score with an AUC of 0. 67 (CI 0. 61-0. 72) in the held out test cohort.
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq
Predicting phenotype from scRNA-seq is challenging for standard machine learning methods -- the number of cells measured can vary by orders of magnitude across individuals and the cell populations are also highly heterogeneous.
High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning
Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis.
Video-based AI for beat-to-beat assessment of cardiac function
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness.
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The Diversity-Innovation Paradox in Science
Are underrepresented groups more likely to generate scientific innovations?
Minimizing Close-k Aggregate Loss Improves Classification
In classification, the de facto method for aggregating individual losses is the average loss.
Inferring Generative Model Structure with Static Analysis
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline.
Accelerated Stochastic Power Iteration
We propose a simple variant of the power iteration with an added momentum term, that achieves both the optimal sample and iteration complexity.
Learning the Structure of Generative Models without Labeled Data
Curating labeled training data has become the primary bottleneck in machine learning.
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Prior work has explored learning accuracies for these sources even without ground truth labels, but they assume that a single accuracy parameter is sufficient to model the behavior of these sources over the entire training set.
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions.
