AIMC Topic: Pattern Recognition, Automated

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Effects of adaptation on numerosity decoding in the human brain.

NeuroImage
Psychophysical studies have shown that numerosity is a sensory attribute susceptible to adaptation. Neuroimaging studies have reported that, at least for relatively low numbers, numerosity can be accurately discriminated in the intra-parietal sulcus....

When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.

Medical image analysis
Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models...

Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition.

Journal of biomedical semantics
BACKGROUND: Gene Ontology (GO) terms represent the standard for annotation and representation of molecular functions, biological processes and cellular compartments, but a large gap exists between the way concepts are represented in the ontology and ...

hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression.

IEEE/ACM transactions on computational biology and bioinformatics
Many biomedical classification problems are multi-label by nature, e.g., a gene involved in a variety of functions and a patient with multiple diseases. The majority of existing classification algorithms assumes each sample with only one class label,...

Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Over the past decade, with the development of machine learning, discrete wavelet transform (DWT) has been widely used in computer-aided epileptic electroencephalography (EEG) signal analysis as a powerful time-frequency tool. But some important probl...

Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns.

International journal of computer assisted radiology and surgery
PURPOSE: For realizing computer-aided diagnosis (CAD) of computed tomography (CT) images, many pattern recognition methods have been applied to automatic classification of normal and abnormal opacities; however, for the learning of accurate classifie...

Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology.

International journal of legal medicine
Sex determination on skeletal remains is one of the most important diagnosis in forensic cases and in demographic studies on ancient populations. Our purpose is to realize an automatic operator-independent method to determine the sex from the bone sh...

An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable.

PloS one
Supervised machine learning methods typically require splitting data into multiple chunks for training, validating, and finally testing classifiers. For finding the best parameters of a classifier, training and validation are usually carried out with...

Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We present a novel method to segment retinal images using ensemble learning based convolutional neural network (CNN) architectures. An entropy sampling technique is used to select informative points thus reducing computational complexity while perfor...