Globally, there is a huge unmet need for effective treatments for neurodegenerative diseases. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the patient population present massive challenges to th...
Robots have a role in addressing the secondary impacts of infectious disease outbreaks by helping us sustain social distancing, monitoring and improving mental health, supporting education, and aiding in economic recovery.
BACKGROUND: Word embeddings are dense numeric vectors used to represent language in neural networks. Until recently, there had been no publicly released embeddings trained on clinical data. Our work is the first to study the privacy implications of r...
The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flo...
Background Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for pathologically diagnosing cervical intraepithelial lesions and invasive cervical cancer, colposcopy-...
The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk o...
1-photon (1p) calcium imaging is an increasingly prevalent method in behavioral neuroscience. Numerous analysis pipelines have been developed to improve the reliability and scalability of pre-processing and ROI extraction for these large calcium ima...
Missing observations are always a challenging problem that we have to deal with in diseases that require follow-up. In hospital records for vesicoureteral reflux (VUR) and recurrent urinary tract infection (rUTI), the number of complete cases is very...
This paper proposes a new chaotic image encryption algorithm. Firstly, an original phased composite chaotic map is used. The comparative study shows that the map cryptographic characteristics are better than the Logistic map, and the map is used as t...
Automatic evaluation of 3D volumes is a topic of importance in order to speed up clinical decision making. We describe a method to classify computed tomography scans on volume level for the presence of non-acute cerebral infarction. This is not a tri...
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