Influence of brain tissue segmentation on disease classifier accuracy
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Streams are significant sources of CO2 to the atmosphere. Estimates of CO2 evasion fluxes (fCO2) from streams typically relate to the free flowing water but exclude geomorphological structures within the stream corridor. We found that gravel bars (GBs) are ...
Many municipalities and public authorities have supported the creation of solar cadastres to map the solar energy-generation potential of existing buildings. Despite advancements in modelling solar potential, most of these tools provide simple evaluations ...
The presence of vegetation can significantly affect the solar irradiation received on building surfaces. Due to the complex shape and seasonal variability of vegetation geometry, this topic has gained much attention from researchers. However, existing meth ...
International Society for Photogrammetry and Remote Sensing2016
Background: Alcohol is a major risk factor for burden of disease and injuries globally. This paper presents a systematic method to compute the 95% confidence intervals of alcohol-attributable fractions (AAFs) with exposure and risk relations stemming from ...
This thesis studies statistical inference in the high energy physics unfolding problem, which is an ill-posed inverse problem arising in data analysis at the Large Hadron Collider (LHC) at CERN. Any measurement made at the LHC is smeared by the finite reso ...
Fully automated machine learning methods based on structural magnetic resonance imaging data can assist radiologists in the diagnosis of Alzheimer's disease (AD). These algorithms require large data sets to learn the separation of subjects with and without ...
The high energy physics unfolding problem is an important statistical inverse problem in data analysis at the Large Hadron Collider (LHC) at CERN. The goal of unfolding is to make nonparametric inferences about a particle spectrum from measurements smeared ...
Background: Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). New method: T1 ma ...
The procedures commonly used to evaluate the performance of objective quality metrics rely on ground truth mean opinion scores and associated confidence intervals, which are usually obtained via direct scaling methods. However, indirect scaling methods, su ...
We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and natur ...