Feature Selection for Big Visual Data: Overview and Challenges

International Conference Image Analysis and Recognition (ICIAR 2018, Póvoa de Varzim, Portugal

Bayesian deep learning for semantic segmentation of food images

This work was partially funded by 20211005001-VRIDT-UCN, TIN2018-095232-B-C21, PID2019-109238GB-C21, Measurer EIT Digital, Logmeal4Shape, and CERCA Programme/Generalitat de Catalunya, Spain. B. Nagarajan acknowledges the support of FPI Becas, MICINN, Spain

Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper presents an exhaustive study about the problem at hand using different texture analysis methods in three colour spaces and different machine learning algorithms. All these methods and classifiers have been tested on a dataset composed of 105 images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated with the benefits of being faster and unaffected by subjective factors, with maximum accuracy over 95%

STAG3 truncating variant as the cause of primary ovarian insufficiency

Primary ovarian insufficiency (POI) is a distressing cause of infertility in young women. POI is heterogeneous with only a few causative genes having been discovered so far. Our objective was to determine the genetic cause of POI in a consanguineous Lebanese family with two affected sisters presenting with primary amenorrhoea and an absence of any pubertal development. Multipoint parametric linkage analysis was performed. Whole-exome sequencing was done on the proband. Linkage analysis identified a locus on chromosome 7 where exome sequencing successfully identified a homozygous two base pair duplication (c.1947_48dupCT), leading to a truncated protein p.(Y650Sfs*22) in the STAG3 gene, confirming it as the cause of POI in this family. Exome sequencing combined with linkage analyses offers a powerful tool to efficiently find novel genetic causes of rare, heterogeneous disorders, even in small single families. This is only the second report of a STAG3 variant; the first STAG3 variant was recently described in a phenotypically similar family with extreme POI. Identification of an additional family highlights the importance of STAG3 in POI pathogenesis and suggests it should be evaluated in families affected with POI

Nuclear organization and 3D chromatin architecture in cognition and neuropsychiatric disorders

The current view of neuroplasticity depicts the changes in the strength and number of synaptic connections as the main physical substrate for behavioral adaptation to new experiences in a changing environment. Although transcriptional regulation is known to play a role in these synaptic changes, the specific contribution of activity-induced changes to both the structure of the nucleus and the organization of the genome remains insufficiently characterized. Increasing evidence indicates that plasticity-related genes may work in coordination and share architectural and transcriptional machinery within discrete genomic foci. Here we review the molecular and cellular mechanisms through which neuronal nuclei structurally adapt to stimuli and discuss how the perturbation of these mechanisms can trigger behavioral malfunction

Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs

The Arteriolar-to-Venular Ratio (AVR) is a popular dimensionless measure which allows the assessment of patients’ condition for the early diagnosis of different diseases, including hypertension and diabetic retinopathy. This paper presents two new approaches for AVR computation in retinal photographs which include a sequence of automated processing steps: vessel segmentation, caliber measurement, optic disc segmentation, artery/vein classification, region of interest delineation, and AVR calculation. Both approaches have been tested on the INSPIRE-AVR dataset, and compared with a ground-truth provided by two medical specialists. The obtained results demonstrate the reliability of the fully automatic approach which provides AVR ratios very similar to at least one of the observers. Furthermore, the semi-automatic approach, which includes the manual modification of the artery/vein classification if needed, allows to significantly reduce the error to a level below the human error