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X-ray Image Classification and Model Evaluation

#artificialintelligence

Kaggle has a wonderful source of chest X-ray image datasets for pneumonia and normal cases. There are significant differences between the image of a normal X-ray and an affected X-ray. Machine learning can play a pivotal role in determining the disease and significantly boost the diagnosis time as well as reduce human effort. I have been motivated by the work done here on the datasets between cats and dogs and reused the code block for dataset pipeline. First we need to import the necessary packages.


Implementing Real-time Object Detection System using PyTorch and OpenCV

#artificialintelligence

The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade. Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together. I know some of you might be thinking why I am using Python, isn't it too slow for a real-time application, and you are right; to some extent. The most compute-heavy operations, like predictions or image processing, are being performed by PyTorch and OpenCV both of which use c behind the scene to implement these operations, therefore it won't make much difference if we use c or python for our use case here.


Architectures for Medical Image Segmentation [Part 2: Attention UNet]

#artificialintelligence

I started writing about network architectures useful for medical image segmentation i.e. In the first article, I had covered basic UNet and 3D UNet. You can find that here. In this article, I'm going to go over Attention UNet. Fully convolutional neural networks (FCNNs) like UNet outperform traditional approaches in medical image analysis.


AI system-on-chip runs on solar power

#artificialintelligence

AI is used in an array of useful applications, such as predicting a machine's lifetime through its vibrations, monitoring the cardiac activity of patients and incorporating facial recognition capabilities into video surveillance systems. The downside is that AI-based technology generally requires a lot of power and, in most cases, must be permanently connected to the cloud, raising issues related to data protection, IT security and energy use. CSEM engineers may have found a way to get around those issues, thanks to a new system-on-chip they have developed. It runs on a tiny battery or a small solar cell and executes AI operations at the edge--i.e., locally on the chip rather than in the cloud. What's more, their system is fully modular and can be tailored to any application where real-time signal and image processing is required, especially when sensitive data are involved.


Facebook can now detect 'the most dangerous crime of the future' and the AI used to make them

The Independent - Tech

Facebook has developed a model to tell when a video is using a deepfake – and can even tell which algorithm was used to create it. The term "deepfake" refers to a video where artificial intelligence and deep learning – an algorithmic learning method used to train computers – has been used to make a person appear to say something they have not. Notable examples of deepfakes include a manipulated video of Richard Nixon's Apollo 11 presidential address and Barack Obama insulting Donald Trump – and although they are relatively benign now, experts suggest that they could be the most dangerous crime of the future. Detecting a deepfake relies on telling whether an image is real or not, but the amount of information available to researchers to do so can be limited – relying on potential input-output pairs or rely on hardware information that might not be available in the real world. Facebook's new process relies in detecting the unique patterns behind an artificially-intelligent model that could generate a deepfake.


Artificial Neural Patches

#artificialintelligence

This article describes what neural patches and patch systems are, their advantage over tradition neural network design, and why we're looking for people to train interesting artificial neural patches for image classification. It goes over the steps to train such patches using a simple Windows tool, how to test them in the wild on mobile devices (iOS and Android) and submit them for publication review. In 2006 researchers used fMRI (functional magnetic resonance imaging) and electrical recordings of individual nerve cells to find regions of the inferior temporal lobe that become active when macaque monkeys observe another monkey's face. They found that some nerve regions are triggered only when a face is identified. And those trigger other regions which show sensitivity to only specific orientations of the face, or to specific feature exaggerations. Such regions of a neural network that are conditionally activated in the presence of certain coarse features, and then extract more finer features, are referred to as Neural Patches.


Artificial Neural Patches

#artificialintelligence

This article describes what neural patches and patch systems are, their advantage over tradition neural network design, and why we're looking for people to train interesting artificial neural patches for image classification. It goes over the steps to train such patches using a simple Windows tool, how to test them in the wild on mobile devices (iOS and Android) and submit them for publication review. In 2006 researchers used fMRI (functional magnetic resonance imaging) and electrical recordings of individual nerve cells to find regions of the inferior temporal lobe that become active when macaque monkeys observe another monkey's face. They found that some nerve regions are triggered only when a face is identified. And those trigger other regions which show sensitivity to only specific orientations of the face, or to specific feature exaggerations. Such regions of a neural network that are conditionally activated in the presence of certain coarse features, and then extract more finer features, are referred to as Neural Patches.


Papers with Code - Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain

#artificialintelligence

In the present paper we present the potential of Explainable Artificial Intelligence methods for decision-support in medical image analysis scenarios. With three types of explainable methods applied to the same medical image data set our aim was to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN)... The visual explanations were provided on in-vivo gastral images obtained from a Video capsule endoscopy (VCE), with the goal of increasing the health professionals' trust in the black box predictions. We implemented two post-hoc interpretable machine learning methods LIME and SHAP and the alternative explanation approach CIU, centered on the Contextual Value and Utility (CIU). The produced explanations were evaluated using human evaluation.


Deep Learning with PyTorch : Neural Style Transfer

#artificialintelligence

In this 2 hour-long project-based course, you will learn to implement neural style transfer using PyTorch. Neural Style Transfer is an optimization technique used to take a content and a style image and blend them together so the output image looks like the content image but painted in the style of the style image. We will create artistic style image using content and given style image. We will compute the content and style loss function. We will minimize this loss function using optimization techniques to get an artistic style image that retains content features and style features.


WildfireNet: Predicting Wildfire Profiles

#artificialintelligence

This excerpt is based on the paper we turned in at the 35th AAAI Student Abstract and Poster Program [7]. In recent years, wildfire has become an unavoidable natural disaster that continues to threaten fire-prone communities. The consequences of massive wildfires are brutal. For instance, in 2003, wildfires that occurred in San Diego County burned over 376,000 acres and 3,241 households. Traditional, physics and empirically-based wildfire spread models have been continuously studied to mitigate losses resulting from wildfire.