Integrative blockwise sparse analysis for tissue characterization and classification [0.03%]
集成块稀疏分析用于组织特征识别和分类
Keni Zheng,Chelsea E Harris,Rachid Jennane et al.
Keni Zheng et al.
The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of clinical imaging patterns int...
Data reduction and data visualization for automatic diagnosis using gene expression and clinical data [0.03%]
利用基因表达和临床数据进行自动诊断的数据压缩与数据可视化
Pierangela Bruno,Francesco Calimeri,Alexandre Sébastien Kitanidis et al.
Pierangela Bruno et al.
Accurate diagnoses of specific diseases require, in general, the review of the whole medical history of a patient. Currently, even though many advances have been made for disease monitoring, domain experts are still requested to perform dir...
Temporal matrix completion with locally linear latent factors for medical applications [0.03%]
具有局部线性潜在因子的医疗应用时间矩阵补全方法
Andy J Ma,Jacky C P Chan,Frodo K S Chan et al.
Andy J Ma et al.
Regular medical records are useful for medical practitioners to analyze and monitor patient's health status especially for those with chronic disease. However, such records are usually incomplete due to unpunctuality and absence of patients...
Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection [0.03%]
平衡卷积神经网络与经典候选检测在胸部射线照片中用于肺结节的检测
Sheng Chen,Yaqi Han,Jinqiu Lin et al.
Sheng Chen et al.
Computer-aided detection (CADe) systems play a crucial role in pulmonary nodule detection via chest radiographs (CXRs). A two-stage CADe scheme usually includes nodule candidate detection and false positive reduction. A pure deep learning m...
Moi Hoon Yap,Manu Goyal,Fatima Osman et al.
Moi Hoon Yap et al.
In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised breast ultrasound image analysis. This task is time consuming and there is inconsistency among...
A novel method for predicting the progression rate of ALS disease based on automatic generation of probabilistic causal chains [0.03%]
一种基于概率因果链自动生成的预测ALS疾病进展速度的新方法
M Ahangaran,M R Jahed-Motlagh,B Minaei-Bidgoli
M Ahangaran
Causal discovery is considered as a major concept in biomedical informatics contributing to diagnosis, therapy, and prognosis of diseases. Probabilistic causality approaches in epidemiology and medicine is a common method for finding relati...
Integrating expert's knowledge constraint of time dependent exposures in structure learning for Bayesian networks [0.03%]
在贝叶斯网络的结构学习中整合专家知识约束的时间依赖暴露因素
Vahé Asvatourian,Philippe Leray,Stefan Michiels et al.
Vahé Asvatourian et al.
Learning a Bayesian network is a difficult and well known task that has been largely investigated. To reduce the number of candidate graphs to test, some authors proposed to incorporate a priori expert knowledge. Most of the time, this a pr...
Missing data imputation and synthetic data simulation through modeling graphical probabilistic dependencies between variables (ModGraProDep): An application to breast cancer survival [0.03%]
通过建模变量之间的图形概率依赖性（ModGraProDep）进行缺失数据插补和合成数据模拟：在乳腺癌生存中的应用
Mireia Vilardell,Maria Buxó,Ramon Clèries et al.
Mireia Vilardell et al.
Background: Two common issues may arise in certain population-based breast cancer (BC) survival studies: I) missing values in a survivals' predictive variable, such as "Stage" at diagnosis, and II) small sample size due t...
A generic approach for cell segmentation based on Gabor filtering and area-constrained ultimate erosion [0.03%]
基于Gabor滤波和面积约束终极腐蚀的细胞分割通用方法
Zihao Wang,Zhenzhou Wang
Zihao Wang
Nowadays, the demand for segmenting different types of cells imaged by microscopes is increased tremendously. The requirements for the segmentation accuracy are becoming stricter. Because of the great diversity of cells, no traditional meth...
A framework to shift basins of attraction of gene regulatory networks through batch reinforcement learning [0.03%]
通过批强化学习转移基因调控网络的吸引盆地的框架
Cyntia Eico Hayama Nishida,Reinaldo A Costa Bianchi,Anna Helena Reali Costa
Cyntia Eico Hayama Nishida
A major challenge in gene regulatory networks (GRN) of biological systems is to discover when and what interventions should be applied to shift them to healthy phenotypes. A set of gene activity profiles, called basin of attraction (BOA), t...