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Found 6 papers, 3 papers with code
Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning
A digital twin is a virtual replica of a real-world physical phenomena that uses mathematical modeling to characterize and simulate its defining features.
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT).
Latent Space Explorer: Visual Analytics for Multimodal Latent Space Exploration
Machine learning models built on training data with multiple modalities can reveal new insights that are not accessible through unimodal datasets.
Generating Drug Repurposing Hypotheses through the Combination of Disease-Specific Hypergraphs
Cosine similarity values are computed between (1) all biological pathways starting at the considered drug and ending at the disease of interest and (2) all biological pathways starting at drugs currently prescribed against that disease and ending at the disease of interest.
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists
We then use a large language model to paraphrase prompt templates that convey the specific tasks to be conducted on each image, and through this process, we create a multi-modal and multi-task training dataset comprising input and output images along with annotated instructions.
Signature Activation: A Sparse Signal View for Holistic Saliency
The adoption of machine learning in healthcare calls for model transparency and explainability.
