134 citations · 210 across the 9 of their papers we have counts for
9 papers
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Amin Adibi, Xu Cao, Zongliang Ji +39
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam,…
Super-Resolution without High-Resolution Labels for Black Hole Simulations
Thomas Helfer, Thomas D. P. Edwards, Jessica Dafflon +2
Generating high-resolution simulations is key for advancing our understanding of one of the universe's most violent events: Black Hole mergers. However, generating Black Hole simul…
Reliability and predictability of phenotype information from functional connectivity in large imaging datasets
Jessica Dafflon, Dustin Moraczewski, Eric Earl +5
One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity a…
Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report
Evi M. C. Huijben, Maarten L. Terpstra, Arthur Jr. Galapon +56
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomogra…
Large language models surpass human experts in predicting neuroscience results
Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun +36
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offe…
Generative AI for Medical Imaging: extending the MONAI Framework
Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot +21
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to he…
Transformer-based normative modelling for anomaly detection of early schizophrenia
Pedro F Da Costa, Jessica Dafflon, Sergio Leonardo Mendes +5
Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly na…
Brain Imaging Generation with Latent Diffusion Models
Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon +5
Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects…
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures
Jessica Dafflon, Walter H. L Pinaya, Federico Turkheimer +7
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to…