Search Results for author:
Found 12 papers, 2 papers with code
Estimating Unknown Population Sizes Using the Hypergeometric Distribution
The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into multiple categories.
The Uncanny Valley: A Comprehensive Analysis of Diffusion Models
Through Diffusion Models (DMs), we have made significant advances in generating high-quality images.
Representational Ethical Model Calibration
Equity is widely held to be fundamental to the ethics of healthcare.
Orientation and Context Entangled Network for Retinal Vessel Segmentation
In this paper, we propose a robust Orientation and Context Entangled Network (denoted as OCE-Net), which has the capability of extracting complex orientation and context information of the blood vessels.
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction
High-quality data accumulation is now becoming ubiquitous in the health domain.
Clusters in Explanation Space: Inferring disease subtypes from model explanations
The distribution of instances in the explanation space of our diagnostic classifier amplifies the different reasons for belonging to the same class - resulting in a representation that is uniquely useful for discovering latent subtypes.
Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis
Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly.
Learning Neural Representations of Human Cognition across Many fMRI Studies
Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets.
Machine learning for precision psychiatry
The nature of mental illness remains a conundrum.
Applications
The Future of Data Analysis in the Neurosciences
Neuroscience is undergoing faster changes than ever before.
Classical Statistics and Statistical Learning in Imaging Neuroscience
This paper portrays commonalities and differences between classical statistics and statistical learning with their relation to neuroimaging research.
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing datasets.
