AIMC Topic: Pattern Recognition, Automated

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Identifying predictive features in drug response using machine learning: opportunities and challenges.

Annual review of pharmacology and toxicology
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...

Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

Neural networks : the official journal of the International Neural Network Society
The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper....

Prediction of the thickness of the compensator filter in radiation therapy using computational intelligence.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (...

Convex nonnegative matrix factorization with manifold regularization.

Neural networks : the official journal of the International Neural Network Society
Nonnegative Matrix Factorization (NMF) has been extensively applied in many areas, including computer vision, pattern recognition, text mining, and signal processing. However, nonnegative entries are usually required for the data matrix in NMF, which...

An SSVEP-Based Brain-Computer Interface for Text Spelling With Adaptive Queries That Maximize Information Gain Rates.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper presents a brain-computer interface for text entry using steady-state visually evoked potentials (SSVEP). Like other SSVEP-based spellers, ours identifies the desired input character by posing questions (or queries) to users through a visu...

Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
P300 spellers can provide a means of communication for individuals with severe neuromuscular limitations. However, its use as an effective communication tool is reliant on high P300 classification accuracies ( > 70%) to account for error revisions. E...

Terrain Classification From Body-Mounted Cameras During Human Locomotion.

IEEE transactions on cybernetics
This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to...

Computer-aided diagnosis from weak supervision: a benchmarking study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Supervised machine learning is a powerful tool frequently used in computer-aided diagnosis (CAD) applications. The bottleneck of this technique is its demand for fine grained expert annotations, which are tedious for medical image analysis applicatio...

Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying thes...

Transfer learning improves supervised image segmentation across imaging protocols.

IEEE transactions on medical imaging
The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervis...