AI Medical Compendium Topic:
Models, Statistical

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Fine-grained leukocyte classification with deep residual learning for microscopic images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past developm...

Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction.

IEEE/ACM transactions on computational biology and bioinformatics
Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the developm...

Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm - support vector regression model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: The refractive index of hemoglobin plays important role in hematology due to its strong correlation with the pathophysiology of different diseases. Measurement of the real part of the refractive index remains a challenge du...

Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

Magnetic resonance in medicine
PURPOSE: Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to asse...

Deep Patient Similarity Learning for Personalized Healthcare.

IEEE transactions on nanobioscience
Predicting patients' risk of developing certain diseases is an important research topic in healthcare. Accurately identifying and ranking the similarity among patients based on their historical records is a key step in personalized healthcare. The el...

Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models.

International journal of computer assisted radiology and surgery
PURPOSE: Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences betw...

A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection.

IEEE/ACM transactions on computational biology and bioinformatics
The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learn...

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases.

Journal of healthcare engineering
Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper...

Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators.

PloS one
Social and economic systems produce complex and nonlinear relationships in the indicator variables that describe them. We present a Bayesian methodology to analyze the dynamical relationships between indicator variables by identifying the nonlinear f...