External validation of SpineNetV2 on a comprehensive set of radiological features for grading lumbosacral disc pathologies.

Background: In recent years, the integration of Artificial Intelligence (AI) models has revolutionized the diagnosis of Low Back Pain (LBP) and associated disc pathologies. Among these, SpineNetV2 stands out as a state-of-the-art, open-access model for detecting and grading various intervertebral disc pathologies. However, ensuring the reliability and applicability of AI models like SpineNetV2 is paramount.

: A synthetic dataset for 3D outdoor light estimation.

In this work, we present a novel dataset, , comprising 39,086 high-resolution images, aimed at addressing the data scarcity in the field of 3D light direction estimation under the assumption of distant lighting. was generated using our software (which is also publicly available), that in turn is based on the Unity3D engine. Our dataset provides a set of images rendered from a given input scene, with the camera moving across a predefined path within the scene.

Exploring ultrasonic communication in mice treated with Cannabis sativa oil: Audio data processing and correlation study with different behaviours.

Studying ultrasonic vocalizations (USVs) plays a crucial role in understanding animal communication, particularly in the field of ethology and neuropharmacology. Communication is associated with social behaviour; so, USVs study is a valid assay in behavioural readout and monitoring in this context. This paper delved into an investigation of ultrasonic communication in mice treated with Cannabis sativa oil (CS mice), which has been demonstrated having a prosocial effect on behaviour of mice, versus control mice (vehicle-treated, VH mice).

Extended performance analysis of deep-learning algorithms for mice vocalization segmentation.

Ultrasonic vocalizations (USVs) analysis represents a fundamental tool to study animal communication. It can be used to perform a behavioral investigation of mice for ethological studies and in the field of neuroscience and neuropharmacology. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and then processed by specific software, which help the operator to identify and characterize different families of calls.

Combining Appearance and Gradient Information for Image Symmetry Detection.

This work addresses the challenging problem of reflection symmetry detection in unconstrained environments. Starting from the understanding on how the visual cortex manages planar symmetry detection, it is proposed to treat the problem in two stages: i) the design of a stable metric that extracts subsets of consistently oriented candidate segments, whenever the underlying 2D signal appearance exhibits definite near symmetric correspondences; ii) the ranking of such segments on the basis of the surrounding gradient orientation specularity, in order to reflect real symmetric object boundaries. Since these operations are related to the way the human brain performs planar symmetry detection, a better correspondence can be established between the outcomes of the proposed algorithm and a human-constructed ground truth.

Automatic classification of mice vocalizations using Machine Learning techniques and Convolutional Neural Networks.

Ultrasonic vocalizations (USVs) analysis is a well-recognized tool to investigate animal communication. It can be used for behavioral phenotyping of murine models of different disorders. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and they are analyzed by specific software.

Learnable Descriptors for Visual Search.

This work proposes LDVS, a learnable binary local descriptor devised for matching natural images within the MPEG CDVS framework. LDVS descriptors are learned so that they can be sign-quantized and compared using the Hamming distance. The underlying convolutional architecture enjoys a moderate parameters count for operations on mobile devices.

FASSEG: A FAce semantic SEGmentation repository for face image analysis.

The FASSEG repository is composed by four subsets containing face images useful for training and testing automatic methods for the task of face segmentation. Threesubsets, namely frontal01, frontal02, and frontal03 are specifically built for performing frontal face segmentation. Frontal01 contains 70 original RGB images and the corresponding roughly labelledground-truth masks.

A tool for the quantification of radial neo-vessels in chick chorioallantoic membrane angiogenic assays.

Angiogenesis, the process of new blood vessels formation, plays a key role in different physiological and pathological conditions and it is considered a promising target for the development of new anti-inflammatory and anti-tumor therapies. Several assays have been developed to mimic the angiogenic process in vitro and in vivo. Here we propose a technique for the quantification of the pro-angiogenic or anti-angiogenic responses induced by different molecules when implanted in vivo on the chick embryo chorioallantoic membrane (CAM).

Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach.

The ability to cross the intestinal cell membrane is a fundamental prerequisite of a drug compound. However, the experimental measurement of such an important property is a costly and highly time consuming step of the drug development process because it is necessary to synthesize the compound first. Therefore, in silico modelling of intestinal absorption, which can be carried out at very early stages of drug design, is an appealing alternative procedure which is based mainly on multivariate statistical analysis such as partial least squares (PLS) and neural networks (NN).