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Found 5 papers, 2 papers with code
Large language models surpass human experts in predicting neuroscience results
LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.
Generative AI for Medical Imaging: extending the MONAI Framework
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
Transformer-based normative modelling for anomaly detection of early schizophrenia
Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0. 82 when assessing the difference between controls and individuals with early-stage schizophrenia.
Brain Imaging Generation with Latent Diffusion Models
In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images.
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures
Here, we apply an autoML library called TPOT which uses a tree-based representation of machine learning pipelines and conducts a genetic-programming based approach to find the model and its hyperparameters that more closely predicts the subject's true age.
