Multivariate Approaches: Joint Modeling of Imaging & Genetic Data

<div><div><div>Talk given as part of the "Introduction to Imaging Genetics" workshop during the 2010 Organization for Human Brain Mapping (OHBM) conference in Barcelona.</div></div></div><div><div></div></div

False Positives in Imaging Genetics Using Nonstationary Cluster-Size Inference

Poster submitted to the 2010 Organization for Human Brain Mapping (OHBM) conference in Barcelona

Additional file 1 of Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case–control study

Additional file 1. Supplementary Figures, Methods and Tables

Distributions of axial ¹⁸F-FDG PET intra slices extracted from the 3D tumor volume of non-responders and responders.

<p>Distributions of axial ¹⁸F-FDG PET intra slices extracted from the 3D tumor volume of non-responders and responders.</p

Kaplan-Meier plot showing the survival rates of responders and non-responders.

<p>Kaplan-Meier plot showing the survival rates of responders and non-responders.</p

Examples of feature maps in the first and last max-pooling layers <b>V</b>⁽¹⁾<b>,</b><b>V</b>⁽⁴⁾ of the CNN architecture.

<p>The feature maps illustrate how a specific triplet is represented in the first and last max-pooling layers.</p

¹⁸F-FDG PET ROIs of a specific tumor <i>i</i> after segmentation embedded into larger square background of standard size of 100 × 100 pixels.

<p>Each enlarged slice is denoted by <b>x</b>_<i>i,j</i> and each set of three spatially adjacent enlarged slides is denoted by <b>z</b>_<i>i,k</i>, where <i>j</i> and <i>k</i> represent the slices and triplets of the specific tumor <i>i</i>. In this example only 3 triplets, from the 5 available slices can be formed, so <i>k</i> = 1,2,3.</p

Classification results: each figure is the average of three independent experiments using different training and test datasets.

<p>Classification results: each figure is the average of three independent experiments using different training and test datasets.</p

Predictive performance of models in Charing Cross (CXC) and Hammersmith (HC) cohorts.

<p>Predictive performance of models in Charing Cross (CXC) and Hammersmith (HC) cohorts.</p

Top ten variable importance using gradient boosting models.

<p>Top ten variable importance using gradient boosting models.</p