A two-layer neural network model that systematically includes correlations among input variables to arbitrary order and is designed to implement Bayes inference has been adapted to classify breast cancer tumors as malignant or benign, assigning a probability for either outcome. The inputs to the network represent measured characteristics of cell nuclei imaged in Fine Needle Aspiration biopsies. The present machine-learning approach to diagnosis (known as HOPP, for higher-order probabilistic perceptron) is tested on the much-studied, open-access Breast Cancer Wisconsin (Diagnosis) Data Set of Wolberg et al. This set lists, for each tumor, measured physical parameters of the cell nuclei of each sample. The HOPP model can identify the key factors -- input features and their combinations -- most relevant for reliable diagnosis. HOPP networks were trained on 90\% of the examples in the Wisconsin database, and tested on the remaining 10\%. Referred to ensembles of 300 networks, selected randomly for cross-validation, accuracy of classification for the test sets of up to 97\% was readily achieved, with standard deviation around 2\%, together with average Matthews correlation coefficients reaching 0.94 indicating excellent predictive performance. Demonstrably, the HOPP is capable of matching the predictive power attained by other advanced machine-learning algorithms applied to this much-studied database, over several decades. Analysis shows that in this special problem, which is almost linearly separable, the effects of irreducible correlations among the measured features of the Wisconsin database are of relatively minor importance, as the Naive Bayes approximation can itself yield predictive accuracy approaching 95\%. The advantages of the HOPP algorithm will be more clearly revealed in application to more challenging machine-learning problems.
Fox News Flash top headlines for Dec. 10 are here. Check out what's clicking on Foxnews.com A Wisconsin state trooper used a drone to find a dog and reunite it with its injured owner following a vehicle crash last week, officials said. Trooper John Jones used his drone to locate River, a 3-year-old Australian Shepherd, in a wooded area in Brown County. The dog and a driver were traveling on State Highway 57 on Friday when a deer darted onto the road, causing the unidentified driver to swerve and crash into a median, the Wisconsin Department of Transportation (DOT) said.
Madison College students Schuyler Bostedt, Garrett Butler and Jonathan Stowell took first place in the Industrial Robotics Competition held during the Wisconsin Manufacturing and Technology Show October 8-10 in Milwaukee. Bostedt, Butler and Stowell are enrolled in the Electromechanical Technology associate degree program. The contest tested robotic programming knowledge by challenging students to complete a set of tasks on a Fanuc education robot in three hours. The objective included program planning, end of arm tool selection, robot programming and teamwork. A panel of experts judged the 23 teams on their ability to complete the assignment, efficiency, teamwork and robotics knowledge.
The use of robots in U.S. workplaces has more than doubled since the Great Recession, but the impact has hit certain areas of the country -- and segments of workers -- more than others. A recent report from The Century Foundation found Midwestern states such as Michigan, Ohio, Indiana, Illinois and Wisconsin saw the sharpest growth in robots being used in the workplace from 2009 to 2017, and these areas now have the highest levels of "robot intensity" in the country. Robot intensity refers to the number of industrial robots per 1,000 human workers. The higher the number, the more robots there are in the workplace alongside humans. Areas with the highest robot intensity are home to some of the of the biggest manufacturing industries in the country.
In 2017, we launched Amazon Transcribe, an automatic speech recognition service that makes it easy for developers to add speech-to-text capability to their applications: today, we're extremely happy to extend it to medical speech with Amazon Transcribe Medical. When I was a child, my parents – both medical doctors – often spent evenings recording letters and exam reports with a microcassette recorder, so that their secretary could later type them and archive them. That was a long time ago, but according to a 2017 study by the University of Wisconsin and the American Medical Association, primary care physicians in the US spend a staggering 6 hours per day entering their medical reports in electronic health record (EHR) systems, now a standard requirement at healthcare providers. I don't think that anyone would argue that doctors should go back to paper reports: working with digital data is so much more efficient. Still, could they be spared these long hours of administrative work?
With a new $1.5 million grant, the growing field of transfer learning has come to the Ming Hsieh Department of Electrical and Computer Engineering at the USC Viterbi School of Engineering. The grant was awarded to three professors -- Salman Avestimehr, Antonio Ortega and Mahdi Soltanolkotabi -- who will work with Ilias Diakonikolas at the University of Wisconsin, Madison, to address the theoretical foundations of this field. Modern machine learning models are breaking new ground in data science, achieving unprecedented performance on tasks like classifying images in one thousand different image categories. This is achieved by training gigantic neural networks. "Neural networks work really well because they can be trained on huge amounts of pre-existing data that has previously been tagged and collected," said Avestimehr, the primary investigator of the project.
Artificial intelligence is making waves in healthcare. More than 40 percent of healthcare executives consider it the technology that will have the greatest impact on their organizations within the next three years, Accenture reports. Some health systems are already using it to address a growing number of issues, from staff efficiencies to predicting patient outcomes that transform the delivery of care. Now, recipients of care are starting to leverage the functionality of AI as well. Consider Froedtert Health and the Medical College of Wisconsin Health Network, which is using Buoy -- an interactive digital tool that allows users to enter their symptoms and receive a personalized analysis and recommendations for care options in real time.
-- Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. M achine learning prediction as an alternative method has shown promising results. This paper present s a method based on a mul tilayer fuzzy expert system for the detection of breast cancer using an e xtreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM - RBF, considering the Wisconsin dataset . The performance of the propose d model is further compared with a l inear - SVM model. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false - negative rate (FNR). The ELM - RBF model for these criteria presents better performance compared to the SVM model . Breast cancer is among the most common disease of young women over the world [1 - 3]. Approximately 29.9% of mortality from can cer in women is due to breast cancer.
Title: Theoretical Foundations of Active Machine Learning Abstract: The field of Machine Learning (ML) has advanced considerably in recent years, but mostly in well-defined domains using huge amounts of human-labeled training data. Machines can recognize objects in images and translate text, but they must be trained with more images and text than a person can see in nearly a lifetime. The computational complexity of training has been offset by recent technological advances, but the cost of training data is measured in terms of the human effort in labeling data. People are not getting faster nor cheaper, so generating labeled training datasets has become a major bottleneck in ML pipelines. Active ML aims to address this issue by designing learning algorithms that automatically and adaptively select the most informative examples for labeling so that human time is not wasted labeling irrelevant, redundant, or trivial examples.