Detection of cells and particles in microscopy images is a common and challenging task. In recent years, detection approaches in computer vision achieved remarkable improvements by leveraging deep learning. Microscopy images pose challenges like smal...
BACKGROUND: Predicting the onset and course of mood and anxiety disorders is of clinical importance but remains difficult. We compared the predictive performances of traditional logistic regression, basic probabilistic machine learning (ML) methods, ...
Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabi...
Anais da Academia Brasileira de Ciencias
Feb 22, 2021
Every day, new applications arise relying on the use of high-resolution road maps in both academic and industrial environments. Autonomous vehicles rely on digital maps to navigate when optical sensors cannot be trusted, such as heavy rainfalls, snow...
Skin cancer is one of the most common and dangerous cancer that exists worldwide. Malignant melanoma is one of the most dangerous skin cancer types has a high mortality rate. An estimated 196,060 melanoma cases will be diagnosed in 2020 in the USA. M...
Seminars in respiratory and critical care medicine
Feb 16, 2021
Pulmonary embolism (PE) remains a diagnostic challenge in 2021. As the pathology is potentially fatal and signs and symptoms are nonspecific, further investigations are classically required. Based on the Bayesian approach, clinical probability became...
A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical ...
Journal of evaluation in clinical practice
Feb 11, 2021
RATIONALE, AIMS AND OBJECTIVES: The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggl...
Neural networks : the official journal of the International Neural Network Society
Feb 5, 2021
Despite the popularism of Bayesian neural networks (BNNs) in recent years, its use is somewhat limited in complex and big data situations due to the computational cost associated with full posterior evaluations. Variational Bayes (VB) provides a usef...
Neural networks : the official journal of the International Neural Network Society
Feb 5, 2021
Latent Dirichlet allocation (LDA) obtains essential information from data by using Bayesian inference. It is applied to knowledge discovery via dimension reducing and clustering in many fields. However, its generalization error had not been yet clari...