Deep Learning for Real-Time Neural Decoding of Grasp
This paper presents a deep learning-based approach for decoding neural signals to classify grip types.
Visit our Scientific Research Area: a section dedicated to professionals in the field, where you can find official resources—official documents, publications, and advanced research—on the scientific research topics of the synbrAIn team.
Analyzing domain shift when using additional data for the MICCAI KiTS23 Challenge
Using additional training data is known to improve the results, especially for medical image 3D segmentation where there is a lack of training material and the model needs to generalize well from few available data.
Automated Stabilization, Enhancement and Capillaries Segmentation in Videocapillaroscopy
Oral capillaroscopy is a critical and non-invasive technique used to evaluate microcirculation. Its ability to observe small vessels in vivo has generated significant interest in the field. Capillaroscopy serves as an essential tool for diagnosing and prognosing various pathologies, with anatomic–pathological lesions playing a crucial role in their progression.
Enhancing video game experience with playtime training and tailoring of virtual opponents: Using Deep Q-Network based Reinforcement Learning on a Multi-Agent Environment
When interacting with fictional environments, the users' sense of immersion can be broken when characters act in mechanical and predictable ways.
Robust and language-independent acoustic features in Parkinson’s disease
The analysis of vocal samples from patients with Parkinson's disease (PDP) can be relevant in supporting early diagnosis and disease monitoring.
Teeth Segmentation in Panoramic Dental X-ray Using Mask Regional Convolutional Neural Network
Accurate instance segmentation of teeth in panoramic dental X-rays is a challenging task due to variations in tooth morphology and overlapping regions. In this study, we propose a new algorithm, for instance, segmentation of the different teeth in panoramic dental X-rays.
Automatic Detection of Myotonia using a Sensory Glove with Resistive Flex Sensors and Machine Learning Techniques
This paper deals with the automatic detection of Myotonia from a task based on the sudden opening of the hand. Data have been gathered from 44 subjects, divided into 17 controls and 27 myotonic patients, by measuring a 2-point articulation of each finger thanks to a calibrated sensory glove equipped with a Resistive Flex Sensor (RFS).
Hallmarks of Parkinson’s disease progression determined by temporal evolution of speech attractors in the reconstructed phase-space
Parkinson’s disease (PD) is one of the most widespread neurodegenerative diseases worldwide, affected by a number of alterations, among which speech impairments that, interestingly, manifests up to 10 years before other major evidences (e.g. motor impairments).
Machine learning- and statistical-based voice analysis of Parkinson’s disease patients: A survey
The preliminary diagnosis and evaluation of the presence and/or severity of Parkinson’s disease is crucial in controlling the progress of the disease.
SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography
Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders.
