AIMC Topic: Pattern Recognition, Automated

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Unsupervised lineage-based characterization of primate precursors reveals high proliferative and morphological diversity in the OSVZ.

The Journal of comparative neurology
Generation of the primate cortex is characterized by the diversity of cortical precursors and the complexity of their lineage relationships. Recent studies have reported miscellaneous precursor types based on observer classification of cell biology f...

Weighting training images by maximizing distribution similarity for supervised segmentation across scanners.

Medical image analysis
Many automatic segmentation methods are based on supervised machine learning. Such methods have proven to perform well, on the condition that they are trained on a sufficiently large manually labeled training set that is representative of the images ...

Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

Medical image analysis
This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more ac...

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution.

Computer methods and programs in biomedicine
Breast cancer is one of the most perilous diseases among women. Breast screening is a method of detecting breast cancer at a very early stage which can reduce the mortality rate. Mammography is a standard method for the early diagnosis of breast canc...

NLP based congestive heart failure case finding: A prospective analysis on statewide electronic medical records.

International journal of medical informatics
BACKGROUND: In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process both structured and unstructured electronic medical records (EMR) to allow complementary and cost-effic...

LogDet Rank Minimization with Application to Subspace Clustering.

Computational intelligence and neuroscience
Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, ...

Using local lexicalized rules to identify heart disease risk factors in clinical notes.

Journal of biomedical informatics
Heart disease is the leading cause of death globally and a significant part of the human population lives with it. A number of risk factors have been recognized as contributing to the disease, including obesity, coronary artery disease (CAD), hyperte...

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

Computational intelligence and neuroscience
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology...

The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

Journal of biomedical informatics
This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utiliz...