Diffeomorphic Information Neural Estimation

Mine: Mutual Information Neural Estimation

We argue that the estimation of the mutual information between high dimensional continuous random variables is achievable by gradient descent over neural networks. This paper presents a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size. MINE is backpropable and we prove that it is strongly consistent. We illustrate a handful of appl...

Diffeomorphic Density Matching by Optimal Information Transport

Hammerstein-Wiener Model: A New Approach to the Estimation of Formal Neural Information

　A new approach is introduced to estimate the formal information of neurons. Formal Information, mainly discusses about the aspects of the response that is related to the stimulus. Estimation is based on introducing a mathematical nonlinear model with Hammerstein-Wiener system estimator. This method of system identification consists of three blocks to completely describe the nonlinearity of inp...

Diffeomorphic Atlas Estimation using Geodesic Shooting on Volumetric Images

In this paper, we propose a new algorithm to compute intrinsic means of organ shapes from 3D medical images. More specifically, we explore the feasibility of Karcher means in the framework of the large deformations by diffeomorphisms (LDDMM). This setting preserves the topology of the averaged shapes and has interesting properties to quantitatively describe their anatomical variability. Estimat...

Diffeomorphic Iterative Centroid Methods for Template Estimation on Large Datasets

A common approach for analysis of anatomical variability relies on the estimation of a template representative of the population. The Large Deformation Diffeomorphic Metric Mapping is an attractive framework for that purpose. However, template estimation using LDDMM is computationally expensive, which is a limitation for the study of large datasets. This paper presents an iterative method which...