Second, we extend the work of Gardner et al.

We introduce a novel information-theoretic approach for active model selection and demonstrate its effectiveness in a real-world application.

In this paper, a convolutional neural network (CNN) was used to classify NMR images of human brains with 4 different types of tumors: meningioma, glioma and pituitary gland tumors. During the training phase of this project, an accuracy of 100% was obtained, meanwhile, in the evaluation phase the precision was 96%.

The rise of precision medicine is being augmented by greater use of deep learning technologies that provide predictive analytics for earlier diagnosis of a range of debilitating diseases. The latest example comes from researchers at Michigan-based Beaumont Health who used deep learning to analyze genomic DNA. The resulting simple blood test could be used to detect earlier onset of Alzheimer's disease. In a study published this week in the peer-reviewed scientific journal PLOS ONE, the researchers said their analysis discovered 152 "significant" genetic differences among Alzheimer's and healthy patients. Those biomarkers could be used to provide diagnoses before Alzheimer's symptoms develop and a patient's brain is irreversibly damaged.

Last year, speaking to The Guardian, Tess Posner, CEO of AI4ALL, an organization that strives to increase diversity within artificial intelligence, concluded that we have reached a "tipping point" in terms of the diversity crisis underlying AI. With every passing day, it gets more challenging to mitigate the biases that are powering AI tools and systems. Now more than ever, awareness and consideration of these biases need to be brought to the forefront of all AI development and implementation. In order to tackle AI biases, it's important to consider the multitude of different types of biases that exist and drive to combat each of them. Racial and ethnic biases pervade AI tools.

Have you ever wondered how to demonstrate that one machine learning model's test set performance differs significantly from the test set performance of an alternative model? This post will describe how to use DeLong's test to obtain a p-value for whether one model has a significantly different AUC than another model, where AUC refers to the area under the receiver operating characteristic. This post includes a hand-calculated example to illustrate all the steps in DeLong's test for a small data set. It also includes an example R implementation of DeLong's test to enable efficient calculation on large data sets. An example use case for DeLong's test: Model A predicts heart disease risk with AUC of 0.92, and Model B predicts heart disease risk with AUC of 0.87, and we use DeLong's test to demonstrate that Model A has a significantly different AUC from Model B with p 0.05.

AI-based tools have transformed from a vague, futuristic vision into actual products that are used on a day-to-day basis to make real-life decisions. Still, for most people, the inner workings of deep-learning systems remain a mystery. If you don't know what exactly is going on while the input data is fed through layer after layer of a neural network, how are you supposed to test the validity of the output? Are the days of simple tests with a clear and understandable result over? First of all, let's make a clear distinction between testing applications that consume AI-based outputs and testing the actual machine learning systems.

Statistical learning on biological data can be challenging due to confounding variables in sample collection and processing. Confounders can cause models to generalize poorly and result in inaccurate prediction performance metrics if models are not validated thoroughly. In this paper, we propose methods to control for confounding factors and further improve prediction performance. We introduce OrthoNormal basis construction In cOnfounding factor Normalization (ONION) to remove confounding covariates and use the Domain-Adversarial Neural Network (DANN) to penalize models for encoding confounder information. We apply the proposed methods to simulated and empirical patient data and show significant improvements in generalization.

Here are a few things you can buy with $200: one bluetooth-controlled fire pit, 100 lab-grown Impossible White Castle sliders, access to the 6.4 billion base pairs that make up all the DNA coiled inside your cells. Starting today, Cambridge-based Veritas Genetics will be lowering its $999 whole genome sequencing and interpretation service for just $199 for two days, or to the first 1,000 people who buy spit kits. Why the dramatic price drop, which Veritas is taking at a loss? CEO Mirza Cifric says that it's more than just a holiday-season gimmick. "We're sending a clear signal to the medical research community that the $99 genome will be here in three to five years," he says.

We introduce a novel information-theoretic approach for active model selection and demonstrate its effectiveness in a real-world application. Although our method can work with arbitrary models, we focus on actively learning the appropriate structure for Gaussian process (GP) models with arbitrary observation likelihoods. We then apply this framework to rapid screening for noise-induced hearing loss (NIHL), a widespread and preventible disability, if diagnosed early. We construct a GP model for pure-tone audiometric responses of patients with NIHL. Using this and a previously published model for healthy responses, the proposed method is shown to be capable of diagnosing the presence or absence of NIHL with drastically fewer samples than existing approaches. Further, the method is extremely fast and enables the diagnosis to be performed in real time.