Scopri il deep learning: reti neurali avanzate, applicazioni AI e il ruolo chiave di questa tecnologia nel futuro dell’innovazione.

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.

Continua a leggereSLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

Parkinson’s Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system.

Continua a leggereArtificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

A Gene Ontology-Driven Wide and Deep Learning Architecture for Cell-Type Classification from Single-Cell RNA-seq Data

Recent advances in single-cell RNA-sequencing in order to study cells in biology, and the increasing amount of data available, led to the development of algorithms for analyzing single cells from gene expression data.

Continua a leggereA Gene Ontology-Driven Wide and Deep Learning Architecture for Cell-Type Classification from Single-Cell RNA-seq Data

synbrAIn annuncia la nuova partnership strategica con EMME ESSE

  • Autore dell'articolo:
  • Categoria dell'articolo:Notizie
  • Tempo di lettura:2 minuti di lettura

Dopo il lancio della piattaforma MS humanAId da parte di EMME ESSE, e il coinvolgimento di synbrAIn nello sviluppo dei moduli di AI, la partnership rappresenta l’inizio di un cammino all’insegna della cooperazione uomo-macchina.

Continua a leggeresynbrAIn annuncia la nuova partnership strategica con EMME ESSE

A Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival Prediction

Glioma is a type of heterogeneous tumor originating in the brain, characterized by the coexistence of multiple subregions with different phenotypic characteristics, which further determine heterogeneous profiles, likely to respond variably to treatment. Identifying spatial variations of gliomas is necessary for targeted therapy.

Continua a leggereA Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival Prediction

SenticLab, partner di ricerca di synbrAIn, ha implementato una soluzione in grado di riconoscere il tipo di tubercolosi a partire da immagini radiografiche

  • Autore dell'articolo:
  • Categoria dell'articolo:Notizie
  • Tempo di lettura:4 minuti di lettura

SenticLab, partner di ricerca di synbrAIn, ha implementato una soluzione in grado di riconoscere il tipo di tubercolosi a partire da immagini radiografiche.

Continua a leggereSenticLab, partner di ricerca di synbrAIn, ha implementato una soluzione in grado di riconoscere il tipo di tubercolosi a partire da immagini radiografiche

Revealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Data

The paper presents and comparatively analyses several deep learning approaches to automatically detect tuberculosis related lesions in lung CTs, in the context of the ImageClef 2020 Tuberculosis task.

Continua a leggereRevealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Data

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