Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defi...
OBJECTIVES: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a model based on tumor depth of in...
Computer methods and programs in biomedicine
Mar 13, 2019
BACKGROUND AND OBJECTIVES: Parkinson's disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting a...
Proceedings of the National Academy of Sciences of the United States of America
Mar 8, 2019
We study multiclass online learning, where a forecaster predicts a sequence of elements drawn from a finite set using the advice of n experts. Our main contributions are to analyze the scenario where the best expert makes a bounded number b of mistak...
BMC medical informatics and decision making
Mar 7, 2019
BACKGROUND: Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel's holiday ...
Proceedings of the National Academy of Sciences of the United States of America
Mar 6, 2019
Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological ...
We propose a method for evolving neural network controllers robust with respect to variations of the environmental conditions (i.e. that can operate effectively in new conditions immediately, without the need to adapt to variations). The method speci...
Today's audiometric methods for the diagnosis of middle ear disease are often based on a comparison of measurements with standard curves, that represent the statistical range of normal hearing responses. Because of large inter-individual variances in...
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patter...
We introduce a novel machine learning approach for investigating speech processing with cochlear implants (CIs)-prostheses used to replace a damaged inner ear. Concretely, we use a simple perceptron and a deep convolutional network to classify speech...