AIMC Topic: Pattern Recognition, Automated

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A categorical analysis of coreference resolution errors in biomedical texts.

Journal of biomedical informatics
BACKGROUND: Coreference resolution is an essential task in information extraction from the published biomedical literature. It supports the discovery of complex information by linking referring expressions such as pronouns and appositives to their re...

A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images.

IEEE transactions on bio-medical engineering
GOAL: In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model.

HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.

NeuroImage
Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodolo...

Minimum Entropy Rate Simplification of Stochastic Processes.

IEEE transactions on pattern analysis and machine intelligence
We propose minimum entropy rate simplification (MERS), an information-theoretic, parameterization-independent framework for simplifying generative models of stochastic processes. Applications include improving model quality for sampling tasks by conc...

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

NeuroImage
Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector ma...

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

Regulatory toxicology and pharmacology : RTP
Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guidelin...

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.

Scientific reports
We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to co...

Enhancing the Prediction of Transmembrane β-Barrel Segments with Chain Learning and Feature Sparse Representation.

IEEE/ACM transactions on computational biology and bioinformatics
Transmembrane β-barrels (TMBs) are one important class of membrane proteins that play crucial functions in the cell. Membrane proteins are difficult wet-lab targets of structural biology, which call for accurate computational prediction approaches. H...

HEp-2 Cell Image Classification With Deep Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which...